-----2
1. John, F., Nash, J.: The bargaining problem. Econometrica 18(2), 155–162 (1950) 
2. Binmore, K., Rubinstein, A., Wolinsky, A.: The Nash bargaining solution in eco- nomic modelling. The RAND Journal of Economics, 176–188 (1986) 
3. Shubik, M.: Game theory in the social sciences: Concepts and solutions. MIT Press (2006) 
4. Raiffa, H.: The Art and Science of Negotiation. Harvard University Press (1982) 
5. Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Wooldridge, M.J., Sierra, C.: Automated negotiation: prospects, methods and challenges. Group Decision and Negotiation 10(2), 199–215 (2001) 
6. Fatima, S.S., Wooldridge, M., Jennings, N.R.: An agenda-based framework for multi-issue negotiation. Artificial Intelligence 152(1), 1–45 (2004) 
7. Zhang, D.: A logic-based axiomatic model of bargaining. Artificial Intelli- gence 174(16-17), 1307–1322 (2010) 
8. Kraus, S., Sycara, K., Evenchik, A.: Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence 104, 1–69 (1998) 
9. Parsons, S., Sierra, C., Jennings, N.R.: Agents that reason and negotiate by argu- ing. Journal of Logic and Computation 8(3), 261–292 (1998) 
10. Luo, X., Jennings, N.R., Shadbolt, N., Leung, H.F., Lee, J.H.M.: A fuzzy con- straint based model for bilateral, multi-issue negotiations in semi-competitive en- vironments. Artificial Intelligence 148(1), 53–102 (2003) A Logical Framework of Bargaining with Integrity Constraints 13 
11. Zhang, D., Zhang, Y.: An ordinal bargaining solution with fixed-point property.Journal of Artificial Intelligence Research 33(1), 433–464 (2008) 
12. Zhan, J., Luo, X., Sim, K.M., Feng, C., Zhang, Y.: A fuzzy logic based model of a bargaining game. In: Wang, M. (ed.) KSEM 2013. LNCS, vol. 8041, pp. 387–403.Springer, Heidelberg (2013) 
13. Konieczny, S., Pe´rez, R.P.: Merging information under constraints: a logical frame- work. Journal of Logic and Computation 12(5), 773–808 (2002) 
14. Zhang, D., Foo, N.: Infinitary belief revision. Journal of Philosophical Logic 30(6), 525–570 (2001) 
15. Lin, J., Mendelzon, A.O.: Merging databases under constraints. International Jour- nal of Cooperative Information Systems 7(01), 55–76 (1998) 
16. Konieczny, S., Pino-Pe´rez, R.: On the logic of merging. In: KR 1998: Principles of Knowledge Representation and Reasoning, pp. 488–498. Morgan Kaufmann (1998) 
-----2
1. Paruchuri, P., Pearce, J.P., Marecki, J., Tambe, M., Ordonez, F., Kraus, S.: Playing games for security: An efficient exact algorithm for solving Bayesian Stackelberg games. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 2, pp. 895–902 (2008) Security Games with Ambiguous Information about Attacker Types 25 
2. Pita, J., Jain, M., Marecki, J., Ordo´n˜ez, F., Portway, C., Tambe, M., Western, C., Paruchuri, P., Kraus, S.: Deployed ARMOR protection: The application of a game theoretic model for security at the Los Angeles International Airport. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems: Industrial Track, pp. 125–132 (2008) 
3. Korzhyk, D., Yin, Z., Kiekintveld, C., Conitzer, V., Tambe, M.: Stackelberg vs.Nash in security games: An extended investigation of interchangeability, equiva- lence, and uniqueness. Journal of Artificial Intelligence Research 41(2), 297–327 (2011) 
4. Tambe, M.: Security and game theory: Algorithms, deployed systems, lessons learned. Cambridge University Press, New York (2011) 
5. Yang, R., Kiekintveld, C., Ordo´n˜Ez, F., Tambe, M., John, R.: Improving resource allocation strategies against human adversaries in security games: An extended study. Artificial Intelligence 195, 440–469 (2013) 
6. Ma, W., Luo, X., Liu, W.: An ambiguity aversion framework of security games under ambiguities. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, pp. 271–278 (2013) 
7. Zimmermann, E.: Globalization and terrorism. European Journal of Political Econ- omy 27(suppl. 1), S152–S161 (2011) 
8. Cronin, A.K.: Behind the curve: Globalization and international terrorism. Inter- national Security 27(3), 30–58 (2003) 
9. Shafer, G.: A mathematical theory of evidence. Princeton University Press, Prince- ton (1976) 
10. Liu, L., Yager, R.R.: Classic works of the Dempster-Shafer theory of belief func- tions: An introduction. In: Yager, R.R., Liu, L. (eds.) Classic Works of the Dempster-Shafer Theory of Belief Functions. Studies in Fuzziness and Soft Com- puting, vol. 219, pp. 1–34. Springer, Heidelberg (2008) 
11. Strat, T.M.: Decision analysis using belief functions. International Journal of Ap- proximate Reasoning 4(5), 391–417 (1990) 
12. Ma, W., Xiong, W., Luo, X.: A model for decision making with missing, imprecise, and uncertain evaluations of multiple criteria. International Journal of Intelligent Systems 28(2), 152–184 (2013) 
13. Xiong, W., Luo, X., Ma, W.: Games with ambiguous payoffs and played by ambi- guity and regret minimising players. In: Thielscher, M., Zhang, D. (eds.) AI 2012.LNCS, vol. 7691, pp. 409–420. Springer, Heidelberg (2012) 
14. Dubois, D., Prade, H.: A note on measures of specificity for fuzzy sets. International Journal of General System 10(4), 279–283 (1985) 
15. Jousselme, A.L., Liu, C., Grenier, D., Bosse, E.: Measuring ambiguity in the evi- dence theory. IEEE Transactions on Systems, Man and Cybernetics, Part A: Sys- tems and Humans 36(5), 890–903 (2006) 
16. Conitzer, V., Sandholm, T.: Computing the optimal strategy to commit to. In: Proceedings of the 7th ACM Conference on Electronic Commerce, pp. 82–90 (2006) 
17. Smets, P., Kennes, R.: The transferable belief model. Artificial Intelligence 66(2), 191–234 (1994) 
18. Eichberger, J., Kelsey, D.: Are the treasures of game theory ambiguous? Economic Theory 48(2-3), 313–339 (2011) 
19. Marco, G.D., Romaniello, M.: Beliefs correspondences and equilibria in ambiguous games. International Journal of Intelligent Systems 27(2), 86–107 (2012) 
20. Wang, C., Tang, W., Zhao, R.: Static Bayesian games with finite fuzzy types and the existence of equilibrium. Information Sciences 178(24), 4688–4698 (2008) 
-----2
1. Alemi, F., Fos, P., Lacorte, W.: A demonstration of methods for studying negotia- tions between physicians and health care managers. Decision Science 21, 633–641 (1990) 
2. Weingart, L.: Knowledge matters–the effect of tactical descriptions on negotia- tion behavior and outcome. Journal of Personality & Social Psychology 70(6), 1205–1217 (1996) 
3. Myerson, R.B., Satterthwaite, M.A.: Efficient mechanisms for bilateral trade. Jour- nal of Economic Theory 29, 265–281 (1983) 
4. Cramton, P.: Strategic delay in bargaining with two-sided uncertainty. Review of Economic Studies 59, 205–225 (1992) 
5. Jarque, X., Ponsati, C., Sakovics, J.: Mediation: incomplete information bargaining with filtered communication. Journal of Mathematical Economics 39(7), 803–830 (2003) 
6. Cramton, P.C.: Dynamic bargaining with transaction costs. Manage. Sci. 37(10), 1221–1233 (1991) 
7. Sandholm, T., Vulkan, N.: Bargaining with deadlines. In: National Conference on Artificial Intelligence, pp. 44–51 (1999) 
8. Ma, C.T.A., Manove, M.: Bargaining with deadlines and imperfect player control.Econometrica 61(6), 1313–1339 (1993) 
9. Rosenschein, J.S., Zlotkin, G.: Rules of Encounter: Designing Conventions for Au- tomated Negotiation Among Computers. MIT Press, Cambridge (1994) 
10. Endriss,U.:Monotonic concession protocols formultilateral negotiation. In:AAMAS 2006:Proceedings of theFifth International JointConference onAutonomousAgents and Multiagent Systems, pp. 392–399. ACM Press, New York (2006) 
11. Raiffa, H.: The Art and Science of Negotiation. Harvard University Press, Cam- bridge (1982) 
12. Fisher, R., Ury, W.: Getting to YES: Negotiating an agreement without giving in.Random House Business Books, New York (1981) 
13. Gal, Y., Pfeffer, A.: Modeling reciprocity in human bilateral negotiation. In: Na- tional Conference on Artificial Intelligence, Vancouver, British Columbia, USA (2007) 
14. Chatterjee, K., Samuelson, L.: Bargaining with two-sided incomplete informa- tion: An infinite horizon model with alternating offers. Review of Economic Stud- ies 54(2), 175–192 (1987) 
15. Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for au- tonomous agents. Int. Journal of Robotics and Autonomous Systems 24(3-4), 159–182 (1998) 
16. Ausubel, L.M., Cramton, P., Deneckere, R.J.: Bargaining with incomplete infor- mation. In: Aumann, R.J., Hart, S. (eds.) Handbook of Game Theory, vol. 3, pp. 1897–1945. Elsevier Science B.V. (2002) 
-----2
1. Dehzangi, A., Phon-Amnuaisuk, S., Dehzangi, O.: Enhancing protein fold pre- diction accuracy by using ensemble of different classifiers. Australian Journal of Intelligent Information Processing Systems 26(4), 32–40 (2010) 
2. Ghanty, P., Pal, N.R.: Prediction of protein folds: Extraction of new features, di- mensionality reduction, and fusion of heterogeneous classifiers. IEEE Transactions on NanoBioscience 8(1), 100–110 (2009) 
3. Deschavanne, P., Tuffery, P.: Enhanced protein fold recognition using a structural alphabet. Proteins: Structure, Function, and Bioinformatics 76(1), 129–137 (2009) 
4. Dehzangi, A., Phon-Amnuaisuk, S., Manafi, M., Safa, S.: Using rotation forest for protein fold prediction problem: An empirical study. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2010. LNCS, vol. 6023, pp. 217–227. Springer, Heidelberg (2010) 
5. Dehzangi, A., Karamizadeh, S.: Solving protein fold prediction problem using fusion of heterogeneous classifiers. INFORMATION, An International Interdisci- plinary Journal 14(11), 3611–3622 (2011) 
6. Dong, Q., Zhou, S., Guan, G.: A new taxonomy-based protein fold recognition ap- proach based on autocross-covariance transformation. Bioinformatics 25(20), 2655– 2662 (2009) 
7. Chmielnicki, W., Stapor, K.: A hybrid discriminative-generative approach to pro- tein fold recognition. Neurocomputing 75(1), 194–198 (2012) 
8. Ding, C., Dubchak, I.: Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17, 349–358 (2001) 
9. Yang, T., Kecman, V., Cao, L., Zhang, C., Huang, J.Z.: Margin-based ensemble classifier for protein fold recognition. Expert Systems with Applications 38, 12348– 12355 (2011) 
10. Kavousi, K., Sadeghi, M., Moshiri, B., Araabi, B.N., Moosavi-Movahedi, A.A.: Evidence theoretic protein fold classification based on the concept of hyperfold.Mathematical Biosciences 240(2), 148–160 (2012) 
11. Shamim, M.T.A., Anwaruddin, M., Nagarajaram, H.A.: Support vector machine- based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs. Bioinformatics 23(24), 3320–3327 (2007) Protein Fold Recognition Using an Overlapping Segmentation Approach 43 
12. Yang, J.Y., Chen, X.: Improving taxonomy-based protein fold recognition by using global and local features. Proteins: Structure, Function, and Bioinformatics 79(7), 2053–2064 (2011) 
13. Dehzangi, A., Phon-Amnuaisuk, S.: Fold prediction problem: The application of new physical and physicochemical- based features. Protein and Peptide Let- ters 18(2), 174–185 (2011) 
14. Sharma, A., Lyons, J., Dehzangi, A., Paliwal, K.K.: A feature extraction tech- nique using bi-gram probabilities of position specific scoring matrix for protein fold recognition. Journal of Theoretical Biology 320, 41–46 (2013) 
15. Dehzangi, A., Sattar, A.: Protein fold recognition using segmentation-based feature extraction model. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013, Part I. LNCS, vol. 7802, pp. 345–354. Springer, Heidelberg (2013) 
16. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J.H., Zhang, Z., Miller, W., Lipman, D.J.: Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Research 17, 3389–3402 (1997) 
17. Shen, H.B., Chou, K.C.: Ensemble classifier for protein fold pattern recognition.Bioinformatics 22, 1717–1722 (2006) 
18. Taguchi, Y.H., Gromiha, M.M.: Application of amino acid occurrence for discrim- inating different folding types of globular proteins. BMC Bioinformatics 8(1), 404 (2007) 
19. Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: Scop: A structural clas- sification of proteins database for the investigation of sequences and structures.Journal of Molecular Biology 247(4), 536–540 (1995) 
20. Kawashima, S., Pokarowska, P.P.M., Kolinski, A., Katayama, T., Kanehisa, M.: Aaindex: Amino acid index database, progress report. Neucleic Acids 36, D202– D205 (2008) 
21. Mathura, V.S., Kolippakkam, D.: Apdbase: Amino acid physico-chemical proper- ties database. Bioinformation 12(1), 2–4 (2005) 
22. Gromiha, M.M.: A statistical model for predicting protein folding rates from amino acid sequence with structural class information. Journal of Chemical Information and Modeling 45(2), 494–501 (2005) 
23. Dehzangi, A., Paliwal, K.K., Sharma, A., Dehzangi, O., Sattar, A.: A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem. IEEE Transaction on Computational Biology and Bioinformatics (TCBB) (in press, 2013) 
24. Liu, T., Geng, X., Zheng, X., Li, R., Wang, J.: Accurate prediction of protein structural class using auto covariance transformation of psi-blast profiles. Amino Acids 42, 2243–2249 (2012) 
25. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer (1999) 
26. Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 1–27 (2011) 
27. Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Tech- niques, 2nd edn. Morgan Kaufmann, San Francisco (2005) 
-----2
1. Balint, A., Fro¨hlich, A.: Improving stochastic local search for SAT with a new probability distribution. In: Strichman, O., Szeider, S. (eds.) SAT 2010. LNCS, vol. 6175, pp. 10–15. Springer, Heidelberg (2010) 
2. Berrera, M., Molinari, H., Fogolari, F.: Amino acid empirical contact energy defi- nitions for fold recognition in the space of contact maps. BMC Bioinformatics 4, 8 (2003) 
3. Bornberg-Bauer, E.: Chain growth algorithms for hp-type lattice proteins. In: Pro- ceedings of the First Annual International Conference on Computational Molecular Biology, RECOMB 1997, pp. 47–55. ACM, New York (1997) 
4. Campeotto, F., Dal Palu`, A., Dovier, A., Fioretto, F., Pontelli, E.: A filtering technique for fragment assembly- based proteins loop modeling with constraints.In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 850–866. Springer, Heidelberg (2012) 
5. Cebria´n, M., Dotu´, I., Van Hentenryck, P., Clote, P.: Protein structure prediction on the face centered cubic lattice by local search. In: Proceedings of the 23rd National Conference on Artificial Intelligence, AAAI 2008, vol. 1, pp. 241–246.AAAI Press (2008) 
6. Cipra, B.: Packing challenge mastered at last. Science 281(5381), 1267 (1998) 
7. Dotu, I., Cebrian, M., Van Hentenryck, P., Clote, P.: On lattice protein struc- ture prediction revisited. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8(6), 1620–1632 (2011) 
8. Kapsokalivas, L., Gan, X., Albrecht, A.A., Steinho¨fel, K.: Population-based local search for protein folding simulation in the MJ energy model and cubic lattices.Computational Biology and Chemistry 33(4), 283–294 (2009) 
9. Lau, K.F., Dill, K.A.: A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules 22(10), 3986–3997 (1989) 
10. Lesh, N., Mitzenmacher, M., Whitesides, S.: A complete and effective move set for simplified protein folding. In: Proceedings of the Seventh Annual International Conference on Research in Computational Molecular Biology, pp. 188–195. ACM, New York (2003) 
11. Lu, H., Yang, G.: Extremal optimization for protein folding simulations on the lattice. Comput. Math. Appl. 57, 1855–1861 (2009) 
12. Mann, M., Will, S., Backofen, R.: CPSP-tools – Exact and complete algorithms for high-throughput 3 D lattice protein studies. Bmc Bioinformatics 9(1), 230 (2008) 
13. Mann, M., Hamra, M.A., Steinho¨fel, K., Backofen, R.: Constraint-based local move definitions for lattice protein models including side chains. In: Proceedings of the Fifth Workshop on Constraint Based Methods for Bioinformatics, WCB 2009 (2009) 
14. Miyazawa, S., Jernigan, R.L.: Estimation of effective interresidue contact en- ergies from protein crystal structures: quasi-chemical approximation. Macro- molecules 18(3), 534–552 (1985) Neighborhood Selection in Constraint-Based Local Search for PSP 55 
15. Newton, M.A.H., Pham, D.N., Sattar, A., Maher, M.: Kangaroo: An efficient constraint-based local search system using lazy propagation. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 645–659. Springer, Heidelberg (2011) 
16. Palu`, A.D., Dovier, A., Fogolari, F., Pontelli, E.: Exploring protein fragment as- sembly using CLP. In: IJCAI, pp. 2590–2595 (2011) 
17. Palu`, A.D., Dovier, A., Pontelli, E.: Heuristics, optimizations, and parallelism for protein structure prediction in CLP(FD). In: PPDP, pp. 230–241 (2005) 
18. Palu`, A.D., Dovier, A., Pontelli, E.: A constraint solver for discrete lattices, its parallelization, and application to protein structure prediction. Softw. Pract. Ex- per. 37, 1405–1449 (2007) 
19. Palu`, A.D., Will, S., Backofen, R., Dovier, A.: Constraint based protein structure prediction exploiting secondary structure information. In: Proceedings of Italian Conference on Computational Logic, CLIC 2004 (2004) 
20. Pham, D.N., Thornton, J., Gretton, C., Sattar, A.: Advances in local search for sat- isfiability. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 213–222. Springer, Heidelberg (2007) 
21. Rashid, M.A., Hoque, M. T., Newton, M.A.H., Pham, D.N., Sattar, A.: A new genetic algorithm for simplified protein structure prediction. In: Thielscher, M., Zhang, D. (eds.) AI 2012. LNCS, vol. 7691, pp. 107–119. Springer, Heidelberg (2012) 
22. Rotkiewicz, P., Skolnick, J.: Fast procedure for reconstruction of full-atom protein models from reduced representations. Journal of Computational Chemistry 29(9), 1460–1465 (2008) 
23. Shatabda, S., Newton, M.A.H., Sattar, A.: Mixed heuristic local search for protein structure prediction. In: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, Bellevue, Washington, USA, July 14-18. AAAI Press (2013) 
24. Shatabda, S., Newton, M., Rashid, M.A., Pham, D.N., Sattar, A.: The road not taken: retreat and diverge in local search for simplified protein structure prediction.BMC Bioinformatics 14(2), 1–9 (2013) 
25. Steinhofel, K., Skaliotis, A., Albrecht, A.: Relating time complexity of protein folding simulation to approximations of folding time. Computer Physics Commu- nications 176(7), 465–470 (2007) 
26. Torres, S.R.D., Romero, D.C.B., Vasquez, L.F.N., Ardila, Y.J.P.: A novel ab-initio genetic-based approach for protein folding prediction. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO 2007, pp.393–400. ACM, New York (2007) 
27. Ullah, A.D., Kapsokalivas, L., Mann, M., Steinho¨fel, K.: Protein folding simulation by two-stage optimization. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009.CCIS, vol. 51, pp. 138–145. Springer, Heidelberg (2009) 
28. Ullah, A.D., Steinho¨fel, K.: A hybrid approach to protein folding problem integrat- ing constraint programming with local search. BMC Bioinformatics 11(S-1), 39 (2010) 
-----2
1. Brohee, S., van Helden, J.: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 7, 488–506 (2006) 
2. Dalvi, B.B., Kshirsagar, M., Sudarshan, S.: Keyword search on external memory data graphs.PVLDB 1(1), 1189–1204 (2008) 
3. Schaeffer, S.: Graph clustering. Computer Science Review 1(1), 27–64 (2007) 
4. Fortunato, S.: Community detection in graphs. Physics Reports 486(3-5), 75–174 (2010) 
5. Satuluri, V., Parthasarathy, S., Ruan, Y.: Local graph sparsification for scalable clustering.In: SIGMOD 2011, pp. 721–732 (2011) 
6. Ngonga Ngomo, A.: Parameter-free clustering of protein-protein interaction graphs. In: Pro- ceedings of Symposium on Machine Learning in Systems Biology 2010 (2010) 
7. Scanniello, G., Marcus, A.: Clustering support for static concept location in source code. In: ICPC, pp. 1–10 (2011) 
8. Karedla, R., Love, J.S., Wherry, B.G.: Caching strategies to improve disk system perfor- mance. Computer 27, 38–46 (1994) 
9. Ngonga Ngomo, A.-C., Schumacher, F.: BorderFlow: A local graph clustering algorithm for natural language processing. In: Gelbukh, A. (ed.) CICLing 2009. LNCS, vol. 5449, pp.547–558. Springer, Heidelberg (2009) 
10. Morsey, M., Lehmann, J., Auer, S., Ngonga Ngomo, A.-C.: DBpedia SPARQL benchmark – performance assessment with real queries on real data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 454–469. Springer, Heidelberg (2011) 
11. Kanjirathinkal, R.C., Sudarshan, S.: Graph clustering for keyword search. In: COMAD (2009) 
12. Kumar, M., Agrawal, K.K., Arora, D.D., Mishra, R.: Implementation and behavioural analy- sis of graph clustering using restricted neighborhood search algorithm. International Journal of Computer Applications 22(5), 15–20 (2011) 
13. Provost, F., Kolluri, V.: A survey of methods for scaling up inductive algorithms. Data Mining and Knowledge Discovery 3, 131–169 (1999) 
14. O’Neil, E.J., O’Neil, P.E., Weikum, G.: The lru-k page replacement algorithm for database disk buffering. SIGMOD Rec. 22, 297–306 (1993) 
15. Breslau, L., Cao, P., Fan, L., Phillips, G., Shenker, S.: Web caching and zipf-like distribu- tions: Evidence and implications. In: INFOCOM, pp. 126–134 (1999) On Caching for Local Graph Clustering Algorithms 67 
16. Karakostas, G., Serpanos, D.N.: Exploitation of different types of locality for web caches. In: Proceedings of the Seventh International Symposium on Computers and Communications, pp. 207–2012 (2002) 
17. Hou, W.-C., Wang, S.: Size-adjusted sliding window LFU - A new web caching scheme. In: Mayr, H.C., Lazansky´, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 567–576. Springer, Heidelberg (2001) 
18. Arlitt, M., Cherkasova, L., Dilley, J., Friedrich, R., Jin, T.: Evaluating content management techniques for web proxy caches. SIGMETRICS Performance Evaluation Review 27(4), 3– 11 (2000) 
19. Tanenbaum, A.S., Woodhull, A.S.: Operating systems - design and implementation, 3rd edn.Pearson Education (2006) 
20. Jin, S., Bestavros, A.: Greedydual* web caching algorithm – exploiting the two sources of temporal locality in web request streams. In: 5th International Web Caching and Content Delivery Workshop, pp. 174–183 (2000) 
21. Schlitter, N., Falkowski, T., La¨ssig, J.: Dengraph-ho: Density-based hierarchical community detection for explorative visual network analysis. In: Springer (ed.) Proceedings of the 31st SGAI International Conference on Artificial Intelligence (2011) 
22. Schaeffer, S.: Stochastic local clustering for massive graphs. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 354–360. Springer, Heidelberg (2005) 
23. Felner, A.: Finding optimal solutions to the graph partitioning problem with heuristic search.Ann. Math. Artif. Intell. 45(3-4), 293–322 (2005) 
24. Alamgir, M., von Luxburg, U.: Multi-agent random walks for local clustering on graphs. In: ICDM, pp. 18–27 (2010) 
25. Spielman, D.A., Teng, S.H.: A local clustering algorithm for massive graphs and its applica- tion to nearly-linear time graph partitioning. CoRR abs/0809.3232 (2008) 
26. Biemann, C., Teresniak, S.: Disentangling from babylonian confusion – unsupervised lan- guage identification. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 773–784.Springer, Heidelberg (2005) 
27. Young, N.E.: On-line file caching. In: Proceedings of the Ninth Annual ACM-SIAM Sym- posium on Discrete Algorithms, pp. 82–86 (1998) 
28. Gavin, A.C., et al.: Proteome survey reveals modularity of the yeast cell machinery. Nature (January 2006) 
29. Ho, Y., et al.: Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry. Nature 415(6868), 180–183 (2002) 
30. Ito, T., et al.: A comprehensive two-hybrid analysis to explore the yeast protein interactome.Proc. Natl. Acad. Sci. U.S.A 98(8), 4569–4574 (2001) 
31. Krogan, N., et al.: Global landscape of protein complexes in the yeast saccharomyces cere- visiae. Nature (March 2006) 
-----2
1. Bai, Q., Su, X., Liu, Q., Terhorst, A., Zhang, M., Mu, Y.: Case-based trust evaluation from provenance information. In: 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 336–343.IEEE (2011) 
2. Bunke, H., Kandel, A.: Mean and maximum common subgraph of two graphs. Pat- tern Recognition Letters 21(2), 163–168 (2000) 
3. Cover, T.M., Thomas, J.A.: Elements of information theory. Wiley, New York (1991) 
4. Freire, J., Koop, D., Santos, E., Silva, C.T.: Provenance for computational tasks: A survey. Computing in Science & Engineering 10(3), 11–21 (2008) 
5. Raymond, J.W., Gardiner, E.J., Willett, P.: Rascal: Calculation of graph similarity using maximum common edge subgraphs. The Computer Journal 45(6), 631–644 (2002) 
6. Renner, R., Maurer, U.: About the mutual (conditional) information. In: Designs, Codes, and Cryptography (2000) 
-----2
1. Baillet, S., Mosher, J.C., Leahy, R.M.: Electromagnetic Brain Mapping. IEEE Sig- nal Processing Magazine 18(6), 14–30 (2001) 
2. Boulton, D.M., Wallace, C.S.: An information measure for hierarchic classification.The Computer Journal 16(3), 254–261 (1973) 
3. Dowe, D.L.: MML, hybrid Bayesian network graphical models, statistical consis- tency, invariance and uniqueness. In: Bandyopadhyay, P.S., Forster, M.R. (eds.) Handbook of the Philosophy of Science. Philosophy of Statistics, vol. 7, pp. 901– 982. Elsevier (2011) 
4. Edwards, R.T., Dowe, D.L.: Single factor analysis in MML mixture modelling. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, pp. 96–109.Springer, Heidelberg (1998) 
5. Grech, R., Cassar, T., Muscat, J., Camilleri, K., Fabri, S., Zervakis, M., Xan- thopoulos, P., Sakkalis, V., Vanrumste, B.: Review on solving the Inverse problem in EEG source analysis. Journal of NeuroEngineering and Rehabilitation 5(1), 25 (2008) 
6. Hallez, H., Vanrumste, B., Grech, R., Muscat, J., Clercq, W.D., Vergult, A., D’Asseler, Y., Camilleri, K.P., Fabri, S.G., Van Huffel, S.: Review on solving the forward problem in EEG source analysis. Journal of NeuroEngineering and Reha- bilitation (1), 46 (2007) 
7. Nunez, P.L., Srinivasan, R.: Electric Fields of the Brain: The neurophysics of EEG.Oxford Scholarship Online (May 2009) 
8. Pascual-Marqui, R.D.: Standardized low resolution brain electromagnetic tomogra- phy (sLORETA): technical details. Technical report, The KEY Institute for Brain- Mind Research, University Hospital of Psychiatry Lenggstr, 31, CH-8029 Zurich, Switzerland (2002) 
9. Phan, T.G., Gureyev, T.E., Nesterets, Y., Ma, H., Thyagarajan, D.: Novel appli- cation of EEG Source Localization in the assessment of the penumbra. Cerebrovas- cular Diseases 33(4), 405–407 (2012) 
10. Ramirez, R.R.: Source Localization. Scholarpedia 3(11), 1733 (2008) 
11. Visser, G., Dowe, D.L.: Minimum Message Length Clustering of Spatially- Correlated Data with Varying Inter-Class Penalties. In: Proc. 6th IEEE Inter- national Conf. on Computer and Information Science (ICIS) 2007, pp. 17–22 (July 2007) 
12. Wallace, C.S.: Intrinsic Classification of Spatially Correlated Data. The Computer Journal 41(8), 602–611 (1998) 
13. Wallace, C.S.: Statistical and Inductive Inference by Minimum Message Length.Springer (May 2005) 
14. Wallace, C.S., Boulton, D.M.: An Information Measure for Classification. The Computer Journal 11(2), 185–194 (1968) 
15. Wallace, C.S., Dowe, D.L.: Intrinsic classification by MML the snob program. In: Proc. Seventh Australian Joint Conf. Artificial Intelligence, pp. 37–44. World Sci- entific (1994) 
16. Wallace, C.S., Dowe, D.L.: MML clustering of multi-state, Poisson, von Mises cir- cular and Gaussian distributions. Statistics and Computing 10(1), 73–83 (2000) 
17. Wu, S.C., Swindlehurst, A.L.: Matching Pursuit and Source Deflation for Sparse EEG/MEG Dipole Moment Estimation. IEEE Transactions on Biomedical Engi- neering PP(99), 1–1 (2013) 
-----2
1. Manolio, T.A.: Genomewide association studies and assessment of the risk of disease.The New England Journal of Medicine 363(2), 166–176 (2010) 
2. Dudoit, S., Shaffer, J.P., Boldrick, J.C.: Multiple hypothesis testing in microarray experiments. Statistical Science 18(1), 71–103 (2003) 
3. Miller, A.J.: Selection of subsets of regression variables. Journal of the Royal Sta- tistical Society (Series A) 147(3), 389–425 (1984) 
4. Dite, G., Jenkins, M., Southey, M., Hocking, J., Giles, G., McCredie, M., Venter, D., Hopper, J.: Familial risks, early-onset breast cancer, and BRCA1 and BRCA2 germline mutations. J. Natl. Cancer Inst. 95, 448–457 (2003) 
5. Odefrey, F., Gurrin, L., Byrnes, G., Apicella, C., Dite, G.: Common genetic variants associated with breast cancer and mammographic density measures that predict disease. Cancer Research 70, 1449–1458 (2010) 
6. Weale, M.: Quality control for genome-wide association studies. Methods Mol.Biol. 628, 341–372 (2010) 
7. Consortium, I.H.: A second generation human haplotype map of over 3.1 million snps. Nature 449, 851–861 (2007) 
-----2
1. Abraham, W.C.: Metaplasticity: tuning synapses and networks for plasticity. Na- ture Reviews Neuroscience 9(5), 387–387 (2008) 
2. Caporale, N., Dan, Y.: Spike timing-dependent plasticity: a hebbian learning rule.Annu. Rev. Neurosci. 31, 25–46 (2008) 
3. Caroni, P., Donato, F., Muller, D.: Structural plasticity upon learning: regulation and functions. Nature Reviews Neuroscience 13(7), 478–490 (2012) 
4. Guise, M., Knott, A., Benuskova, L.: Consistency of polychronous neural group ac- tivation supports a role as an underlying mechanism for representation and mem- ory: detailed methods and results. Tech. rep., Dept of Computer Science, University of Otago, Dunedin (2013) 
5. Hoffmann, H., Howard, M.D., Daily, M.J.: Fast pattern matching with time-delay neural networks. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 2424–2429. IEEE (2011) 
6. Izhikevich, E.M.: Polychronization: computation with spikes. Neural Computa- tion 18(2), 245–282 (2006) 
7. Izhikevich, E.M.: Reference software implementation for the Izhikevich model: min- imal spiking network that can polychronize (2006), http://www.izhikevich.org/publications/spnet.htm 
8. Izhikevich, E.M., Gally, J.A., Edelman, G.M.: Spike-timing dynamics of neuronal groups. Cerebral Cortex 14(8), 933–944 (2004) 
9. Martin, S., Grimwood, P., Morris, R.: Synaptic plasticity and memory: an evalua- tion of the hypothesis. Annual Review of Neuroscience 23(1), 649–711 (2000) 
10. Martinez, R., Paugam-Moisy, H.: Algorithms for structural and dynamical poly- chronous groups detection. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part II. LNCS, vol. 5769, pp. 75–84. Springer, Heidelberg (2009) 
-----2
1. Duncan, J., Humphreys, G.W.: Visual search and stimulus similarity. Psychological Re- view 91(3), 433–458 (1989) 
2. Nieder, A., Miller, E.K.: A parieto-frontal network for visual numerical information in the monkey. Proceedings of the National Acadamey of Sciences 101(19), 7457–7462 (2004) 
3. Izard, V., Dehaene-Lambertz, G., Dehaene, S.: Distinct cerebral pathways for object identity and number in human infants. PLoS Biology 6(2), 275–285 (2008) 
4. Walles, H., Knott, A., Robins, A.: A model of cardinality blindness in inferotemporal cortex.Biological Cybernetics 98(5), 427–437 (2008) 
5. Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognitive Psychol- ogy 12, 97–136 (1980) 
6. Moran, J., Desimone, R.: Selective attention gates visual processing in the extrastriate cortex.Science 229(4715), 782–784 (1985) 
7. Zhang, Y., Meyers, E., Bichot, N., Serre, T., Poggio, T., Desimone, R.: Object decoding with attention in inferior temporal cortex. Proceedings of the National Academy of Sciences of the USA 108(21), 8850–8855 (2011) 
8. Kadir, T., Brady, M.: Saliency, scale and image description. International Journal of Com- puter Vision 45(2), 83–105 (2001) 
9. Kadir, T., Hobson, P., Brady, M.: From salient features to scene description. In: Workshop on Image Analysis for Multimedia Interactive Services (2005) 
10. Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nature Neuroscience 2(11), 1019–1025 (1999) 
11. Fink, G., Halligan, P., Marshall, J., Frith, C., Frackowiak, R., Dolan, R.: Where in the brain does visual attention select the forest and the trees. Nature 382, 626–628 (1996) 
12. Flevaris, A., Bentin, S., Robertson, L.: Local or global? attentional selection of spatial fre- quencies binds shapes to hierarchical levels. Psychological Science 21(3), 424–431 (2010) 
13. Flevaris, A., Bentin, S., Robertson, L.: Attention to hierarchical level influences attentional selection of spatial scale. Journal of Experimental Psychology: Human Perception and Per- formance 37(1), 12–22 (2011) 
14. Logothetis, N.K., Sheinberg, D.L.: Visual object recognition. Annual Review of Neuro- science 19, 577–621 (1996) 
15. Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40, 1489–1506 (2000) 
16. Moore, T., Armstrong, K.M.: Selective gating of visual signals by microstimulation of frontal cortex. Nature 421, 370–373 (2003) 
17. Sowden, P., Schyns, P.: Channel surfing in the visual brain. Trends in Cognitive Sci- ences 10(12), 538–545 (2006) 
18. Walles, H., Robins, A., Knott, A.: A neural network model of visual attention and object classification: technical details. Technical Report OUCS-2013-09, Dept of Computer Sci- ence, University of Otago (2013) 
19. Treisman, A.: Perceptual grouping and attention in visual search for features and for objects.Journal of Experimental Psychology: HPP 8(2), 194–214 (1982) 
20. Treisman, A., Gormican, S.: Feature analysis in early vision: Evidence from search asymme- tries. Psychological Review 95(1), 15–48 (1988) 
-----2
1. Endrass, B., Rehm, M., André, E.: Planning Small Talk Behavior with Cultural Influences  for Multiagent Systems. Computer Speech and Language 25(2), 158–174 (2011)  
2. Zhang, L., Jiang, M., Farid, D., Hossain, A.M.: Intelligent Facial Emotion Recognition and  Semantic-based Topic Detection for a Humanoid Robot. Expert Systems with  Applications 40(13), 5160–5168 (2013)  
3. Liu, H., Singh, P.: ConceptNet: A practical commonsense reasoning toolkit. BT  Technology Journal 22 (2004)  
4. Ptaszynski, M., Dybala, P., Shi, W., Rzepka, R., Araki, K.: Towards Context Aware  Emotional Intelligence in Machines: Computing Contextual Appropriateness of Affective  States. In: Proceeding of IJCAI (2009)  
5. Mateas, M.: Ph.D. Thesis. Interactive Drama, Art and Artificial Intelligence. School of  Computer Science, Carnegie Mellon University (2002)  
6. Cavazza, M., Smith, C., Charlton, D., Zhang, L., Turunen, M., Hakulinen, J.A.:  ‘Companion’ ECA with Planning and Activity Modelling. In: Proc. of 7th Int. Conf. on  Autonomous Agents and Multiagent Systems (AAMAS), Estoril, Portugal, pp. 1281–1284  (2008)  
7. Landauer, T.K., Dumais, S.: Latent semantic analysis. Scholarpedia 3(11), 4356 (2008)  
8. Widdows, D., Cohen, T.: The Semantic Vectors Package: New Algorithms and Public  Tools for Distributional Semantics. In: IEEE Int. Conference on Semantic Computing  (2010)  
9. Olsson, F.: Bootstrapping Named Entity Recognition by Means of Active Machine  Learning. PhD thesis, University of Gothenburg (2008)  
10. Zhang, L.: Contextual and Active Learning-based Affect-sensing from Virtual Drama  Improvisation. ACM Transactions on Speech and Language Processing (TSLP) 9(4),  Article No. 8 (2013)  
-----2
1. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary pat- terns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006) 
2. Al-Sahaf, H., Song, A., Neshatian, K., Zhang, M.: Extracting image features for classification by two-tier genetic programming. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012) 
3. Biederman, I.: Recognition-by-components: A theory of human image understand- ing. Psychological Review 94, 115–147 (1987) 
4. Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and tex- ture of real-world surfaces. ACM Transactions on Graphics 18(1), 1–34 (1999) 
5. Duin, R.P.: Small sample size generalization. In: Proceedings of the Ninth Scandi- navian Conference on Image Analysis, Uppsala, Sweden, vol. 2, pp. 957–964 (1995) 
6. Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 594–611 (2006) 
7. Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 264–271 (June 2003) 
8. Hegenbart, S., Maimone, S., Uhl, A., Ve´csei, A., Wimmer, G.: Customised fre- quency pre-filtering in a local binary pattern-based classification of gastrointesti- nal images. In: Greenspan, H., Mu¨ller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 99–109. Springer, Heidelberg (2013) 
9. Jain, A.K., Chandrasekaran, B.: Dimensionality and sample size considerations in pattern recognition practice. In: Classification Pattern Recognition and Reduction of Dimensionality, vol. 2, pp. 835–855. Elsevier (1982) 
10. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992) 
11. Kylberg, G.: The Kylberg texture dataset v. 1.0. External report (Blue series) 35, Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, Uppsala, Sweden (2011) 
12. Lake, B.M., Salakhutdinov, R., Gross, J., Tenenbaum, J.B.: One shot learning of simple visual concepts. In: Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Austin, TX, pp. 2568–2573 (2011) 
13. Liu, L., Zhao, L., Long, Y., Kuang, G., Fieguth, P.: Extended local binary patterns for texture classification. Image and Vision Computing 30(2), 86–99 (2012) 122 H. Al-Sahaf, M. Zhang, and M. Johnston 
14. Luke, S.: Essentials of Metaheuristics, 2nd edn. Lulu (2013), http://cs.gmu.edu/~sean/book/metaheuristics/ 
15. Montana, D.J.: Strongly typed genetic programming. Evolutionary Computa- tion 3(2), 199–230 (1995) 
16. Ojala, T., Pietika¨inen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996) 
17. Ojala, T., Pietika¨inen, M., Ma¨enpa¨a¨, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002) 
18. Pietika¨inen, M., Hadid, A., Zhao, G., Ahonen, T.: Local binary patterns for still images. In: Computer Vision Using Local Binary Patterns. Computational Imaging and Vision, vol. 40, pp. 13–47. Springer London (2011) 
19. Porter, F.C.: Testing Consistency of Two Histograms. ArXiv e-prints, pp. 1–35 (2008) 
20. Raudys, S.J., Jain, A.K.: Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(3), 252–264 (1991) 
21. Salakhutdinov, R., Tenenbaum, J.B., Torralba, A.: One-shot learning with a hier- archical nonparametric bayesian model. Journal of Machine Learning Research - Proceedings Track 27, 195–206 (2012) 
22. Schneiderman, H., Kanade, T.: A statistical method for 3d object detection applied to faces and cars. In: Proceedings of Computer Vision and Pattern Recognition, pp. 1746–1759. IEEE Computer Society (2000) 
23. Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: Proceeding of Computer Vision and Pattern Recognition, pp. 511–518.IEEE Computer Society (2001) 
24. Weber, M., Welling, M., Perona, P.: Unsupervised learning of models for recog- nition. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 18–32. Springer, Heidelberg (2000) 
25. Xie, J., Zhang, D., You, J., Zhang, D.: Texture classification via patch-based sparse texton learning. In: IEEE International Conference on Image Processing (ICIP), pp. 2737–2740 (2010) 
26. Yip, K., Sussman, G.J.: Sparse representations for fast, one-shot learning. In: Pro- ceedings of the Fourteenth National Conference on Artificial Intelligence, pp. 521– 527. AAAI Press / The MIT Press (1997) 
-----2
1. Nguyen, H.T., Ji, Q., Smeulders, A.W.: Spatio-temporal context for robust multi- target tracking. IEEE TPAMI 29(1), 52–64 (2007) 
2. Andrade, E.L., Blunsden, S., Fisher, R.B.: Modelling crowd scenes for event detec- tion. In: Proc. ICPR 2006, vol. 1, pp. 175–178 (2006) 
3. Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event de- tection using multiple fixed-location monitors. IEEE TPAMI 30(3), 555–560 (2008) 
4. Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: Proc. CVPR 2009, pp. 1446–1453 (2009) 134 H. Lin, J.D. Deng, and B.J. Woodford 
5. Ke, Y., Sukthankar, R., Hebert, M.: Event detection in crowded videos. In: Proc.ICCV 2007, pp. 1–8 (2007) 
6. Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: Proc. CVPR 2009, pp. 935–942 (2009) 
7. Wu, S., Moore, B.E., Shah, M.: Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: Proc. CVPR 2010, pp. 2054–2060 (2010) 
8. Cong, Y., Yuan, J., Liu, J.: Sparse reconstruction cost for abnormal event detection.In: Proc. CVPR 2011, pp. 3449–3456 (2011) 
9. Tziakos, I., Cavallaro, A., Xu, L.Q.: Event monitoring via local motion abnormality detection in non-linear subspace. Neurocomputing 73(10), 1881–1891 (2010) 
10. Thida, M., Eng, H.-L., Dorothy, M., Remagnino, P.: Learning video manifold for segmenting crowd events and abnormality detection. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 439–449. Springer, Heidelberg (2011) 
11. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003) 
12. Thida, M., Eng, H.L., Monekosso, D.N., Remagnino, P.: Learning video manifolds for content analysis of crowded scenes. IPSJ Transactions on Computer Vision and Applications 4, 71–77 (2012) 
13. Liu, C., Freeman, W.T., Adelson, E.H., Weiss, Y.: Human-assisted motion anno- tation. In: Proc. CVPR 2008, pp. 1–8 (2008) 
14. Niyogi, X.: Locality preserving projections. Neural Information Processing Sys- tems 16, 153 (2004) 
15. Golub, G.H., van Loan, C.F.: Matrix computations (1996) 
16. Scho¨lkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Esti- mating the support of a high-dimensional distribution. Neural Computation 13(7), 1443–1471 (2001) 
17. Garate, C., Bilinsky, P., Bremond, F.: Crowd event recognition using hog tracker.In: Proc. PETS-Winter 2009, pp. 1–6 (2009) 
18. Chan, A.B., Morrow, M., Vasconcelos, N.: Analysis of crowded scenes using holistic properties. In: Proc. PETS-Winter 2009, pp. 101–108 (2009) 
19. Shi, Y., Gao, Y., Wang, R.: Real-time abnormal event detection in complicated scenes. In: Proc. ICPR 2010, pp. 3653–3656 (2010) 
-----2
1. Bouwmans, T., Baf, F.E., Vachon, B.: Statistical Background Modeling for Fore- ground Detection: A Survey, pp. 181–199. World Scientific Publishing (2010) 
2. Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and Prac- tice of Background Maintenance. In: Proceedings of the IEEE International Con- ference on Computer Vision, ICCV 1999, vol. 1, p. 255. IEEE Computer Society (1999) 
3. Stauffer, C., Grimson, W.: Adaptive Background Mixture Models for Real-Time Tracking. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1999, vol. 2, pp. 246–252. IEEE Computer Society (1999) 
4. KaewTraKulPong, P., Rowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Proceedings of the Second European Workshop on Advanced Video Based Surveillance Systems, pp. 149–158 (2001) 
5. Shah, M., Deng, J., Woodford., B.: Enhancing the Mixture of Gaussians back- ground model with local matching and local adaptive learning. In: Proceedings of the 27th Conference on Image and Vision Computing New Zealand (IVCNZ 2012), pp. 103–108. ACM (2012) 
6. Heikkila, M., Pietikainen, M.: A Texture-Based Method for Modeling the Back- ground and Detecting Moving Objects. IEEE Transaction on Pattern Analysis and Machine Intelligence 28(4), 657–662 (2006) 
7. Bouwmans, T., Baf, F.E., Vachon, B.: Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey. Recent Patents on Computer Science 1(3), 219–237 (2008) Growing Neural Gas Video Background Model (GNG-BM) 147 
8. Fritzke, B.: A Growing Neural Gas Network Learns Topologies. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 625–632. MIT Press (1995) 
9. Martinetz, T., Berkovich, S., Schulten, K.: ‘Neural-gas’ network for vector quanti- zation and its application to time-series prediction. IEEE Transactions on Neural Networks 4(4), 558–569 (1993) 
10. Martinetz, T.: Competitive hebbian learning rule forms perfectly topology pre- serving maps. In: Proceedings of the International Conference on Artificial Neural Networks (ICANN 1993), pp. 427–434. Springer London (1993) 
11. Qin, A.K., Suganthan, P.N.: Robust growing neural gas algorithm with application in cluster analysis. Neural Networks 17(8-9), 1135–1148 (2004) 
12. Fritzke, B.: A self-organizing network that can follow non-stationary distributions.In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997.LNCS, vol. 1327, pp. 613–618. Springer, Heidelberg (1997) 
13. Evangelio, R.H., Ptzold, M., Sikora, T.: Splitting Gaussians in Mixture Models.In: Proceedings of the 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012, pp. 300–305. IEEE Computer Society (2012) 
14. Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.: Background and fore- ground modeling using nonparametric kernel density estimation for visual surveil- lance. Proceedings of the IEEE 90(7), 1151–1163 (2002) 
15. Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background Segmentation with Feed- back: The Pixel-Based Adaptive Segmenter. In: Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recognition -Change Detection Workshop, pp. 38–43. IEEE (2012) 
16. Goyette, N., Jodoin, P., Porikli, F., Konrad, J., Ishwar, P.: Changedetection.net: A new change detection benchmark dataset. In: Proceedings of the IEEE Com- puter Society Conference on Computer Vision and Pattern Recognition (CVPR) -Workshops, pp. 1–8. IEEE Computer Society (2012) 
-----2
1. Yang, J., Zhang, D., Frangi, A.F., Yang, J.Y.: Two-dimensional, P.C.A.: a new approach to appearance-based face representation and recognition. IEEE Transac- tions on Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004) 
2. Jolliffe, I.: Principal component analysis. John Wiley & Sons Ltd. (2005) 
3. Richman, M.B.: Rotation of principal components. Journal of Climatology 6(3), 293–335 (1986) 
4. Vines, S.K.: Simple principal components. Journal of the Royal Statistical Society: Series C (Applied Statistics) 49(4), 441–451 (2000) 
5. Jeffers, J.N.R.: Two case studies in the application of principal component analysis.Applied Statistics 225–236 (1967) 
6. Zou, H., Hastie, T., Tibshirani, R.: Sparse principal component analysis. Journal of Computational and Graphical Statistics 15(2), 265–286 (2006) 
7. Alter, O., Brown, P.O., Botstein, D.: Singular value decomposition for genome-wide expression data processing and modeling. Proceedings of the National Academy of Sciences 97(18), 10101–10106 (2000) 
8. Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 267–288 (1996) 
9. Cadima, J., Jolliffe, I.T.: Loading and correlations in the interpretation of principle compenents. Journal of Applied Statistics 22(2), 203–214 (1995) 
10. Nie, F., Huang, H., Cai, X., Ding, C.H.: Efficient and robust feature selection via joint L2,1-norms minimization. In: Proc. NIPS, pp. 1813–1821 (2010) 
11. Gu, Q., Li, Z., Han, J.: Joint feature selection and subspace learning. In: Proceed- ings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 2. AAAI Press (2011) 
12. Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodol- ogy) 68(1), 49–67 (2006) 
13. Argyriou, A., Evgeniou, T., Pontil, M.: Convex multi-task feature learning. Ma- chine Learning 73(3), 243–272 (2008) 
14. Obozinski, G., Taskar, B., Jordan, M.I.: Joint covariate selection and joint subspace selection for multiple classification problems. Statistics and Computing 20(2), 231– 252 (2010) 
15. Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(1), 40–51 (2007) Sparse Principal Component Analysis 159 
16. Hou, C., Nie, F., Yi, D., Wu, Y.: Feature selection via joint embedding learning and sparse regression. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 2. AAAI Press (2011) 
17. Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. The Annals of Statistics 32(2), 407–499 (2004) 
18. Journe, M., Nesterov, Y., Richtrik, P., Sepulchre, R.: Generalized power method for sparse principal component analysis. The Journal of Machine Learning Research 11, 517–553 (2010) 
19. Jenatton, R., Audibert, J.Y., Bach, F.: Structured variable selection with sparsity- inducing norms. The Journal of Machine Learning Research 12, 2777–2824 (2011) 
20. Zou, H., Hastie, T.: Regression shrinkage and selection via the elastic net, with applications to microarrays. Journal of the Royal Statistical Society: Series B 67, 301–320 (2003) 
21. Trefethen, L.N., Bau III, D.: Numerical linear algebra. SIAM (1997) 
-----2
1. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In: Proceedings of the 2001 IEEE International Conference on Computer Vision, vol. 2, pp. 416–423 (2001) 
2. Peherstorfer, B., Pflu¨ger, D., Bungartz, H.-J.: A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps.In: Wang, D., Reynolds, M. (eds.) AI 2011. LNCS, vol. 7106, pp. 112–121. Springer, Heidelberg (2011) 
3. von Luxburg, U.: A Tutorial on Spectral Clustering. Statistics and Comput- ing 17(4), 395–416 (2007) 
4. Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral Grouping Using the Nystrom Method. IEEE Transactions on Pattern Analysis and Machine Intelli- gence 26(2), 214–225 (2004) 
5. Alzate, C., Suykens, J.A.K.: Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(2), 335–347 (2010) 
6. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003) 
7. Bungartz, H.J., Griebel, M.: Sparse grids. Acta Numerica 13, 147–269 (2004) 
8. Pflu¨ger, D.: Spatially Adaptive Sparse Grids for High-Dimensional Problems. Ver- lag Dr. Hut, Mu¨nchen (August 2010) 
9. Bengio, Y., Paiement, J.F., Vincent, P.: Out-of-sample extensions for LLE, isomap, MDS, eigenmaps, and spectral clustering. In: Advances in Neural Information Pro- cessing Systems, pp. 177–184. MIT Press (2003) 
10. Wu, X.: Efficient Statistical Computations for Optimal Color Quantization. In: Arvo, J. (ed.) Graphics Gems II, pp. 126–133. Academic Press (1991) 
11. Adorf, J.: Nonlinear Clustering on Sparse Grids. Studienarbeit/SEP/IDP, Institut fu¨r Informatik, Technische Universita¨t Mu¨nchen (August 2012) 
-----2
1. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In: Proceedings of the 2001 IEEE International Conference on Computer Vision, vol. 2, pp. 416–423 (2001) 
2. Peherstorfer, B., Pflu¨ger, D., Bungartz, H.-J.: A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps.In: Wang, D., Reynolds, M. (eds.) AI 2011. LNCS, vol. 7106, pp. 112–121. Springer, Heidelberg (2011) 
3. von Luxburg, U.: A Tutorial on Spectral Clustering. Statistics and Comput- ing 17(4), 395–416 (2007) 
4. Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral Grouping Using the Nystrom Method. IEEE Transactions on Pattern Analysis and Machine Intelli- gence 26(2), 214–225 (2004) 
5. Alzate, C., Suykens, J.A.K.: Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(2), 335–347 (2010) 
6. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003) 
7. Bungartz, H.J., Griebel, M.: Sparse grids. Acta Numerica 13, 147–269 (2004) 
8. Pflu¨ger, D.: Spatially Adaptive Sparse Grids for High-Dimensional Problems. Ver- lag Dr. Hut, Mu¨nchen (August 2010) 
9. Bengio, Y., Paiement, J.F., Vincent, P.: Out-of-sample extensions for LLE, isomap, MDS, eigenmaps, and spectral clustering. In: Advances in Neural Information Pro- cessing Systems, pp. 177–184. MIT Press (2003) 
10. Wu, X.: Efficient Statistical Computations for Optimal Color Quantization. In: Arvo, J. (ed.) Graphics Gems II, pp. 126–133. Academic Press (1991) 
11. Adorf, J.: Nonlinear Clustering on Sparse Grids. Studienarbeit/SEP/IDP, Institut fu¨r Informatik, Technische Universita¨t Mu¨nchen (August 2012) 
-----2
1. Balint, A., Fro¨hlich, A.: Improving stochastic local search for SAT with a new probability distribution. In: Strichman, O., Szeider, S. (eds.) SAT 2010. LNCS, vol. 6175, pp. 10–15. Springer, Heidelberg (2010) 
2. Balint, A., Fro¨hlich, A., Tompkins, D.A., Hoos, H.H.: Sparrow 2011. In: Booklet of SAT 2011 Competition (2011) 
3. Cai, S., Su, K.: Configuration checking with aspiration in local search for sat. In: AAAI (2012) 
4. Duong, T.-T., Pham, D.N., Sattar, A.: A method to avoid duplicative flipping in local search for SAT. In: Thielscher, M., Zhang, D. (eds.) AI 2012. LNCS, vol. 7691, pp. 218–229. Springer, Heidelberg (2012) 
5. Duong, T.T., Pham, D.N., Sattar, A.: A study of local minimum avoidance heuris- tics for sat. In: ECAI, pp. 300–305 (2012) 
6. Duong, T.T.N., Pham, D.N., Sattar, A., Newton, M.A.H.: Weight-enhanced diver- sification in stochastic local search for satisfiability. In: IJCAI, pp. 524–530 (2013) 
7. Gableske, O., Heule, M.J.H.: EagleUP: Solving random 3-SAT using SLS with unit propagation. In: Sakallah, K.A., Simon, L. (eds.) SAT 2011. LNCS, vol. 6695, pp.367–368. Springer, Heidelberg (2011) 
8. Hoos, H.H.: An adaptive noise mechanism for WalkSAT. In: Proceedings of AAAI 2002, pp. 635–660 (2002) 
9. Hutter, F., Hoos, H.H., Leyton-Brown, K., Stu¨tzle, T.: Paramils: An automatic algorithm configuration framework. J. Artif. Intell. Res (JAIR) 36, 267–306 (2009) 
10. McAllester, D.A., Selman, B., Kautz, H.A.: Evidence for invariants in local search.In: AAAI/IAAI, pp. 321–326 (1997) 
11. Pham, D.N., Duong, T.T., Sattar, A.: Trap avoidance in local search using pseudo- conflict learning. In: AAAI, pp. 542–548 (2012) 
12. Pham, D.N., Thornton, J., Gretton, C., Sattar, A.: Combining adaptive and dy- namic local search for satisfiability. JSAT 4(2-4), 149–172 (2008) 
13. Prestwich, S.D.: Random walk with continuously smoothed variable weights. In: Bacchus, F., Walsh, T. (eds.) SAT 2005. LNCS, vol. 3569, pp. 203–215. Springer, Heidelberg (2005) 
14. Selman, B., Kautz, H.A.: Domain-independent extensions to gsat: Solving large structured satisfiability problems. In: IJCAI, pp. 290–295 (1993) 
15. Selman, B., Levesque, H.J., Mitchell, D.G.: A new method for solving hard satis- fiability problems. In: AAAI, pp. 440–446 (1992) 
16. Thornton, J.R., Pham, D.N., Bain, S., Ferreira Jr., V.: Additive versus multiplica- tive clause weighting for SAT. In: Proceedings of AAAI 2004, pp. 191–196 (2004) 
17. Wei, W., Li, C.M.: Switching between two adaptive noise mechanisms in lo- calsearch. In: Booklet of the 2009 SAT Competition (2009) 
18. Wei, W., Li, C.M., Zhang, H.: A switching criterion for intensification and diversi- fication in local search for SAT. JSAT 4(2-4), 219–237 (2008) 
-----2
1. Lo´pez, C.L., Junghanns, A.: Perimeter Search Performance. In: Schaeffer, J., Mu¨ller, M., Bjo¨rnsson, Y. (eds.) CG 2002. LNCS, vol. 2883, pp. 345–359. Springer, Heidel- berg (2003) 
2. Korf, R.E., Reid, M., Edelkamp, S.: Time complexity of iterative-deepening-A*.Artificial Intelligence 129(1-2), 199–218 (2001) 
3. Zahavi, U., Felner, A., Burch, N., Holte, R.C.: Predicting the performance of IDA* with conditional distributions. In: Fox, D., Gomes, C.P. (eds.) AAAI Conference on Artificial Intelligence (AAAI 2008), pp. 381–386. AAAI Press (2008) 
4. Zahavi, U., Felner, A., Schaeffer, J., Sturtevant, N.R.: Inconsistent heuristics. In: AAAI Conference on Artificial Intelligence (AAAI 2007), pp. 1211–1216 (2007) 
5. Domshlak, C., Karpas, E., Markovitch, S.: To max or not to max: Online learning for speeding up optimal planning. In: AAAI Conference on Artificial Intelligence (AAAI 2010), pp. 1701–1706 (2010) 
6. Tolpin, D., Beja, T., Shimony, S.E., Felner, A., Karpas, E.: Towards rational de- ployment of multiple heuristics in a*. CoRR abs/1305.5030 (2013) 
7. Haslum, P., Botea, A., Helmert, M., Bonet, B., Koenig, S.: Domain-independent construction of pattern database heuristics for cost-optimal planning. In: AAAI Conference on Artificial Intelligence (AAAI-2007), vol. 22(2), p. 1007. AAAI Press, MIT Press, Menlo Park, Cambridge (2007) 
8. Helmert, M., Haslum, P., Hoffmann, J.: Flexible abstraction heuristics for optimal sequential planning. In: Proceedings ICAPS 2007, pp. 176–183 (2007) 
9. Felner, A., Korf, R.E., Hanan, S.: Additive pattern database heuristics. Journal of Artificial Intelligence Research (JAIR) 22, 279–318 (2004) 
-----2
1. Bazzan, A.L., Klu¨gl, F.: Re-routing Agents in an Abstract Traffic Scenario (2008) 
2. Zhu, S., Levinson, D., Zhang, L.: An agent-based route choice model. Working Papers 000089, University of Minnesota: Nexus Research Group (2007) 
3. Sadek, A.W., Smith, B.L., Demetsky, M.J.: Dynamic traffic assignment: Genetic algo- rithms approach. Transportation Research Record: Journal of the Transportation Research Board 1588, 95–103 (1997) 
4. Cruz, F., van Woensel, T., Smith, J.M., Lieckens, K.: On the system optimum of traffic assignment in state-dependent queueing networks. European Journal of Operational Re- search 201(1), 183–193 (2010) 
5. D’Acierno, L., Montella, B., De Lucia, F.: A stochastic traffic assignment algorithm based on ant colony optimisation. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stu¨tzle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 25–36. Springer, Heidelberg (2006) 
6. de Dios Ortuzar, J., Willumsen, L.G.: Modelling Transport, 2nd edn. John Willey & Sons (1994) 
7. Fricker, J.D., Whitford, R.K.: Fundamentals of transportation engineering: a multimodal ap- proach. Pearson Prentice Hall, Upper Saddle River (2004) 
8. Bazzan, A.L.: Traffic as a Complex System: Four Challenges for Computer Science and Engineering (2007) 
9. Chen, O., Ben-Akiva, M.: Game-Theoretic Formulations of Interaction Between Dynamic Traffic Control and Dynamic Traffic Assignment. Transportation Research Record: Journal of the Transportation Research Board 1617(-1), 179–188 (1998) 
10. Kitamura, R., Nakayama, S.: Can travel time information influence network flow? - Implica- tions of the minority game. Transportation Research Record, 14–20 (2007) 
11. Challet, D., Zhang, Y.C.: Emergence of cooperation and organization in an evolutionary game. Physica A 246(3-4), 12 (1997) 
12. Galib, S.M., Moser, I.: Road traffic optimisation using an evolutionary game (2011) 
13. Dehmer, M., Barbarini, N., Varmuza, K., Graber, A.: A Large Scale Analysis of Information- Theoretic Network Complexity Measures Using Chemical Structures. PLoS ONE 4(12), e8057 (2009) 
14. Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001) 
15. Constantine, G.: Graph complexity and the laplacian matrix in blocked experiments. Linear and Multilinear Algebra 28, 8 (1990) 
16. Jukna, S.: On graph complexity. Combinatorics, Probability and Computing 15, 22 (2006) 
17. Kim, J., Wilhelm, T.: What is a complex graph? Physica A: Statistical Mechanics and its Applications 387(11), 2637–2652 (2008) 
18. da F. Costa, L., Rodrigues, F., Travieso, G.: Characterization of complex networks: A survey of measurements. Advances in Physics 56(1), 76 (2007) 
-----2
1. Abdullah, S., et al.: Investigating Ahuja–Orlin’s large neighbourhood search approach for  examination timetabling. Spectr. 29(2), 351–372 (2006)  
2. Brailsford, S.: Constraint satisfaction problems: Algorithms and applications. Eur. J. Oper.  Res. 119(3), 557–581 (1999)  
3. Burke, E., et al.: A Time-Predefined Local Search Approach to Exam Timetabling Prob- lems, vol. 1153, pp. 76–90 (2003)  
4. Burke, E., et al.: Hybrid Graph Heuristics within a Hyper-Heuristic Approach to Exam  Timetabling Problems (2005)   Constraint Optimization for Timetabling Problems 201  
5. Burke, E., Bykov, Y.: A Late Acceptance Strategy in Hill-Climbing for Exam Timetabling  Problems. Presented at the PATAT 2008 Proceedings of the 7th International Conference  on the Practice and Theory of Automated Timetabling (2008)  
6. Burke, E.K., Newall, J.P., Weare, R.F.: A memetic algorithm for university exam timetabl- ing. In: Burke, E.K., Ross, P. (eds.) PATAT 1995. LNCS, vol. 1153, pp. 241–250. Sprin- ger, Heidelberg (1996)  
7. Burke, E.K., et al.: Adaptive selection of heuristics for improving exam timetables. Ann.  Oper. Res., 1–17 (2012)  
8. Burke, E.K., et al.: Hybrid variable neighbourhood approaches to university exam time- tabling. Eur. J. Oper. Res. 206(1), 46–53 (2010)  
9. Caramia, M., Dell’Olmo, P., Italiano, G.F.: New Algorithms for Examination Timetabling.  In: Näher, S., Wagner, D. (eds.) WAE 2000. LNCS, vol. 1982, pp. 230–241. Springer,  Heidelberg (2001)  
10. Carter, M.W., et al.: Examination Timetabling: Algorithmic Strategies and Applications. J.  Oper. Res. Soc. 47(3), 373 (1996)  
11. Casey, S., Thompson, J.: GRASPing the Examination Scheduling Problem. In: Burke,  E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 232–244. Springer,  Heidelberg (2003)  
12. Craenen, B.G.W., et al.: Comparing evolutionary algorithms on binary constraint satisfac- tion problems. IEEE Trans. Evol. Comput. 7(5), 424–444 (2003)  
13. Craenen, B.G.W.: Solving constraint satisfaction problems with evolutionary algorithms.  Phd Dissertation, Vrije Universiteit (2005)  
14. Demeester, P., et al.: A hyperheuristic approach to examination timetabling problems:  benchmarks and a new problem from practice. J. Sched. 15(1), 83–103 (2012)  
15. Eley, M.: Ant algorithms for the exam timetabling problem. In: Burke, E.K., Rudová, H.  (eds.) PATAT 2007. LNCS, vol. 3867, pp. 364–382. Springer, Heidelberg (2007)  
16. Merlot, L.T.G., Boland, N., Hughes, B.D., Stuckey, P.J.: A Hybrid Algorithm for the Ex- amination Timetabling Problem. In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002.  LNCS, vol. 2740, pp. 207–231. Springer, Heidelberg (2003)  
17. Müller, T.: Constraint-based Timetabling. PhD Dissertation, Charles University (2005)  
18. Qu, R., et al.: A survey of search methodologies and automated system development for  examination timetabling. J. Sched. 12(1), 55–89 (2008)  
19. Shang, Y., Fromherz, M.P.J.: Experimental complexity analysis of continuous constraint  satisfaction problems. Inf. Sci. 153, 1–36 (2003)  
20. Sharma, A., Sharma, D.: ICHEA for Discrete Constraint Satisfaction Problems. In: Thiel- scher, M., Zhang, D. (eds.) AI 2012. LNCS, vol. 7691, pp. 242–253. Springer, Heidelberg  (2012)  
21. Sharma, A., Sharma, D.: Solving Dynamic Constraint Optimization Problems Using  ICHEA. In: Huang, T., Zeng, Z., Li, C., Leung, C.S., et al. (eds.) ICONIP 2012, Part III.  LNCS, vol. 7665, pp. 434–444. Springer, Heidelberg (2012)  
22. Yang, Y., Petrovic, S.: A Novel Similarity Measure for Heuristic Selection in Examination  Timetabling. In: Burke, E.K., Trick, M.A. (eds.) PATAT 2004. LNCS, vol. 3616, pp. 247– 269. Springer, Heidelberg (2005)  
23. Benchmark Exam Timetabling Datasets, http://www.cs.nott.ac.uk/~rxq/  data.htm  
-----2
1. National road safety strategy 2011-2020. Australian Transport Council (May 2011) 
2. A.D. of Infrastructure and Transport, “Road deaths australia 2011 statistical sum- mary” (May 2012), http://www.bitre.gov.au/publications/2012/files/RDA_Summary_2011.pdf 
3. Shahabi, C., Yan, D.: Real-time pattern isolation and recognition over immersive sensor data streams. In: Proceedings of the 9th International Conference on Multi- Media Modeling, pp. 93–113 (2003) 
4. Guralnik, V., Srivastava, J.: Event detection from time series data. In: Proceedings of the fifth ACM SIGKDD International Conference on Knowledge Discovery and Data mining, KDD 1999, pp. 33–42. ACM, New York (1999) 
5. Wahlstrom, E., Masoud, O., Papanikolopoulos, N.: Vision-based methods for driver monitoring. In: 2003 IEEE Proceedingsof Intelligent Transportation Systems., vol. 2, pp. 903–908. IEEE (2003) 
6. Oliver, N., Pentland, A.P.: Graphical models for driver behavior recognition in a smartcar. In: Proceedings of the IEEE Intelligent Vehicles Symposium, IV 2000, pp. 7–12. IEEE (2000) Learning Risky Driver Behaviours by GP 213 
7. Horovitz, O., Krishnaswamy, S., Gaber, M.M.: A fuzzy approach for interpretation of ubiquitous data stream clustering and its application in road safety. Intelligent Data Analysis 11(1), 89–108 (2007) 
8. Lu, H., Yang, J., Liu, Z., Lane, N., Choudhury, T., Campbell, A.: The jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pp. 71–84. ACM (2010) 
9. Dai, J., Teng, J., Bai, X., Shen, Z., Xuan, D.: Mobile phone based drunk driving detection. In: 2010 4th International Conference on-NO PERMISSIONS Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 1–8. IEEE (2010) 
10. Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming.Lulu Enterprises, UK Ltd. (2008) 
11. Wagner, N., Michalewicz, Z.: An analysis of adaptive windowing for time series forecasting in dynamic environments: further tests of the dyfor gp model. In: Pro- ceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO 2008, pp. 1657–1664. ACM, New York (2008) 
12. Kaboudan, M.: Spatiotemporal forecasting of housing prices by use of genetic pro- gramming. In: The 16th Annual Meeting of the Association of Global Business (2004) 
13. Hetland, M.L., Sætrom, P.: Temporal rule discovery using genetic programming and specialized hardware. Applications and Science in Soft Computing 24, 87 (2004) 
14. Song, A., Pinto, B.: Study of gp representations for motion detection with unstable background. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1– 8. IEEE (2010) 
15. Xie, F., Song, A., Ciesielski, V.: Event detection in time series by genetic pro- gramming. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (June 2012) 
16. Ling, C., Huang, J., Zhang, H.: Auc: a better measure than accuracy in comparing learning algorithms. In: Advances in Artificial Intelligence, pp. 991–991 (2003) 
17. Liu, C., Berry, P.M., Dawson, T.P., Pearson, R.G.: Selecting thresholds of occur- rence in the prediction of species distributions. Ecography 28(3), 385–393 (2005) 
18. Quinlan, J.R.: C4. 5: programs for machine learning, vol. 1. Morgan Kaufmann (1993) 
19. John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers.In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann Publishers Inc. (1995) 
20. Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991) 
21. Hastie, T., Tibshirani, R.: Classification by pairwise coupling. In: Proceedings of the 1997 Conference on Advances in Neural Information Processing Systems 10, NIPS 1997, pp. 507–513. MIT Press, Cambridge (1998) 
22. Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm. In: ICML, vol. 96, pp. 148–156 (1996) 
-----2
1. Dash, M., Liu, H.: Feature selection for classification. Intelligent Data Analysis 1, 131–156 (1997) 
2. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003) 
3. Kennedy, J., Eberhart, R.: Particle swarm optimization. IEEE International Conference on Neural Networks 4, 1942–1948 (1995) 
4. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: IEEE International Conference on Evolutionary Computation (CEC1998), pp. 69–73 (1998) 
5. Xue, B., Zhang, M., Browne, W.: Particle swarm optimization for feature selection in classi- fication: A multi-objective approach. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics (2012), doi:10.1109/TSMCB.2012.2227469 220 M.C. Lane et al.
6. Wang, X., Yang, J., Teng, X., Xia, W.: Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters 28, 459–471 (2007) 
7. Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108 (1997) 
8. Bach, F.R., Jordan, M.I.: A probabilistic interpretation of canonical correlation analysis.Technical report (2005) 
9. Pledger, S., Arnold, R.: Multivariate methods using mixtures: correspondence analy- sis, scaling and pattern detection. Computational Statistics and Data Analysis (2013), http://dx.doi.org/10.1016/j.csda.2013.05.013 
10. Matechou, E., Liu, I., Pledger, S., Arnold, R.: Biclustering models for ordinal data. In: Pre- sentation at the NZ Statistical Assn. Annual Conference. University of Auckland (2011) 
11. Bache, K., Lichman, M.: UCI Machine Learning Repository (2013) 
-----2
1. Forrest, S., Mitchell, M.: Relative building-block fitness and the building-block hy- pothesis. In: Whitley, L. (ed.) Foundations of Genetic Algorithms, pp. 109–126 (1993) 
2. Skinner, C., Riddle, P.: Random search can outperform mutation. In: IEEE Congress on Evolutionary Computation, CEC 2007 (2007) 
3. Skinner, C.: On the discovery, selection and combination of building blocks in evo- lutionary algorithms. PhD thesis, Citeseer (2009) 
4. Meadows, B., Riddle, P., Skinner, C., Barley, M.: Evaluating the seeding genetic algorithm (2013), http://www.cs.auckland.ac.nz/~pat/AI2013-long.pdf 10 For real-world domains, ‘best’ parameters will be unknown, but we note that default parameters of 2-point crossover, 0.9 crossover rate, 0.2 seed probability, a seed pool of 50 and a presample of 100,000 surpassed the CGA on all of our problems.11 The fraction of high-quality individuals in the next generation can be calculated in terms of their parents, making the rate fixed over a run. This gives the SGA an advantage over other GA extensions which only delay early convergence.12 A replacement scheme that varied the seed probability throughout the run might deal better with the increasing homogenisation of fitter individuals.Evaluating the Seeding Genetic Algorithm 227 
5. De Jong, K.A.: Analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan Ann Arbor, MI (1975) 
6. Mitchell, M., Forrest, S.: B. 2.7. 5: Fitness landscapes: Royal road functions. Hand- book of evolutionary computation (1997) 
7. Watson, R.A., Pollack, J.B.: Recombination without respect: Schema combination and disruption in genetic algorithm crossover. In: Proceedings of Genetic and Evo- lutionary Computation Conference (GECCO), pp. 112–119 (2000) 
8. Goldberg, D.E.: Simple genetic algorithms and the minimal, deceptive problem.Genetic Algorithms and Simulated Annealing 74 (1987) 
9. Cohen, P., Kim, J.: A bootstrap test for comparing performance of programs when data are censored, and comparisons to Etzioni’s test. Technical report, University of Massachusetts (1993) 
-----2
1. Benko¨, G., Flamm, C., Stadler, P.F.: A graph-based toy model of chemistry. Jour- nal of Chemical Information and Computer Sciences 43(4), 1085–1093 (2003), http://pubs.acs.org/doi/abs/10.1021/ci0200570 pMID: 12870897 
2. Benko¨, G., Flamm, C., Stadler, P.F.: The toychem package: A computational toolkit implementing a realistic artificial chemistry model (2005), http://www.tbi.univie.ac.at/~xtof/ToyChem/ 
3. Channon, A.: Unbounded evolutionary dynamics in a system of agents that actively process and transform their environment. Genetic Programming and Evolvable Machines 7(3), 253–281 (2006), doi:10.1007/s10710-006-9009-3 
4. Daylight Chemical Information Systems, I.: Daylight theory manual (2011), http://www.daylight.com/dayhtml/doc/theory/index.html 
5. Dittrich, P., Ziegler, J., Banzhaf, W.: Artificial chemistries-a review. Artificial Life 7(3), 225–275 (2001), http://www.mitpressjournals.org/doi/abs/10.1162/106454601753238636 
6. Ducharme, V., Egli, R., Legault, C.Y.: Energy-based artificial chemistry simulator.In: Adami, C., Bryson, D.M., Ofria, C., Pennock, R.T. (eds.) Proceedings of the Thirteenth International Conference on the Simulation and Synthesis of Living Systems (Artificial Life 13), pp. 449–456 (2012) 
7. Faulconbridge, A.: RBN-World: sub-symbolic artificial chemistry for artificial life.Ph.D. thesis, University of York (2011) 
8. Fontana, W., Wagner, G.P., Buss, L.W.: Beyond digital naturalism. Artificial Life 1(2), 211–227 (1994) 
9. Landrum, G.: Rdkit: Open-source cheminformatics (2013), http://www.rdkit.org 
10. Maley, C.: Four steps toward open-ended evolution. In: GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1336–1343. Morgan Kaufmann (1999) 
-----2
1. Clune, J.: Heuristic Evaluation Functions for General Game Playing. In: AAAI, pp. 1134–1139. AAAI Press (2007) 
2. Cox, E., Schkufza, E., Madsen, R., Genesereth, M.: Factoring General Games using Propositional Automata. In: Proceedings of the IJCAI Workshop on General Game Playing, GIGA 2009 (2009) 
3. Finnsson, H., Bjornsson, Y.: Simulation-based Approach to General Game Playing.In: AAAI. AAAI Press (2008) 
4. Finnsson, H., Bjornsson, Y.: CadiaPlayer: Search-Control Techniques. KI 25(1), 9–16 (2011) 
5. Genesereth, M., Love, N., Pell, B.: General game playing: Overview of the AAAI competition. AI Magazine 26, 62–72 (2005) 
6. Gunther, M., Schiffel, S.: Dresden General Game Playing Server, http://ggpserver.general-game-playing.de 
7. Haufe, S., Michulke, D., Schiffel, S., Thielscher, M.: Knowledge-Based General Game Playing. KI 25(1), 25–33 (2011) 
8. Haufe, S., Thielscher, M.: Pushing the Envelope: General Game Players Prove Theorems. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 1–10. Springer, Heidelberg (2010) 
9. Kissmann, P., Edelkamp, S.: Instantiating General Games Using Prolog or Depen- dency Graphs. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds.) KI 2010. LNCS, vol. 6359, pp. 255–262. Springer, Heidelberg (2010) 
10. Love, N., Hinrichs, T., Haley, D., Schkufza, E., Genesereth, M.: General Game Play- ing: Game Description Language Specification. Technical report, Stanford Logic Group (2008) 
11. Mehat, J., Cazenave, T.: A Parallel General Game Player. KI 25(1), 43–47 (2011) 
12. Michulke, D., Thielscher, M.: Neural Networks for State Evaluation in General Game Playing. In: Buntine, W., Grobelnik, M., Mladenic´, D., Shawe-Taylor, J.(eds.) ECML PKDD 2009, Part II. LNCS, vol. 5782, pp. 95–110. Springer, Heidel- berg (2009) 
13. Mo¨ller, M., Schneider, M., Wegner, M., Schaub, T.: Centurio, a General Game Player: Parallel, Java- and ASP-based. KI 25(1), 17–24 (2011) 
14. Saffidine, A., Cazenave, T.: A Forward Chaining Based Game Description Lan- guage Compiler. In: IJCAI Workshop on General Intelligence in Game-Playing Agents (GIGA 2011), pp. 69–75 (2011) 
15. Schiffel, S., Thielscher, M.: Fluxplayer: A Successful General Game Player. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI 2007), pp. 1191–1196. AAAI Press (2007) 
16. Sharma, S., Kobti, Z., Goodwin, S.: General Game Playing with Ants. In: Li, X., et al. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 381–390. Springer, Heidelberg (2008) 
17. Sharma, S., Kobti, Z., Goodwin, S.: Knowledge Generation for Improving Simu- lations in UCT for General Game Playing. In: Wobcke, W., Zhang, M. (eds.) AI 2008. LNCS (LNAI), vol. 5360, pp. 49–55. Springer, Heidelberg (2008) 
18. Thielscher, M.: A General Game Description Language for Incomplete Information Games. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 994– 999. AAAI Press (2010) 
19. Thielscher, M.: General Game Playing in AI Research and Education. In: Bach, J., Edelkamp, S. (eds.)KI 2011. LNCS, vol. 7006, pp. 26–37. Springer, Heidelberg (2011) 
20. Waugh, K.: Faster State Manipulation in General Games using Generated Code.In: IJCAI Workshop on General Game Playing, GIGA 2009 (2009) 
-----2
1. Chang, F.S.H., Jackson, D.: Symbolic model checking of declarative relational models. In: Osterweil, L.J., Rombach, H.D., Soffa, M.L. (eds.) ICSE, pp. 312–320. ACM (2006) 
2. Clark, K.L.: Negation as Failure. In: Gallaire, H., Minker, J. (eds.) Logic and Data Bases, pp.292–322. Plenum Press, New York (1978) 
3. Clarke, E.M., Emerson, E.A.: Design and synthesis of synchronization skeletons using branching time temporal logic. In: Kozen, D. (ed.) Logic of Programs 1981. LNCS, vol. 131, pp. 52–71. Springer, Heidelberg (1982) 
4. Fagin, R., Halpern, J.Y., Moses, Y., Vardi, M.Y.: Reasoning About Knowledge. The MIT Press, Cambridge (1995) 
5. Gammie, P., van der Meyden, R.: MCK: Model checking the logic of knowledge. In: Alur, R., Peled, D.A. (eds.) CAV 2004. LNCS, vol. 3114, pp. 479–483. Springer, Heidelberg (2004) 
6. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Kowalski, R., Bowen, K. (eds.) Proceedings of IJCSLP, pp. 1070–1080. MIT Press, Seattle (1988) 
7. Genesereth, M., Love, N., Pell, B.: General game playing: Overview of the AAAI competi- tion. AI Magazine 26(2), 62–72 (2005) 
8. Haufe, S., Schiffel, S., Thielscher, M.: Automated verification of state sequence invariants in general game playing. Artificial Intelligence Journal 187-188, 1–30 (2012) 
9. Haufe, S., Thielscher, M.: Automated verification of epistemic properties for general game playing. In: Proceedings of KR (2012) 
10. Kissmann, P., Edelkamp, S.: Gamer, a general game playing agent. KI 25(1), 49–52 (2011) 
11. Lloyd, J.: Foundations of Logic Programming, 2nd edn. Springer (1987) 
12. Love, N., Hinrichs, T., Haley, D., Schkufza, E., Genesereth, M.: General Game Playing: Game Description Language Specification. Tech. Rep. LG–2006–01, Stanford (2006) 
13. Manna, Z., Pnueli, A.: The Temporal Logic of Reactive and Concurrent Systems. Springer, Berlin (1992) 
14. van der Meyden, R., Gammie, P., Baukus, K., Lee, J., Luo, C., Huang, X.: User manual for mck 1.0.0. Tech. rep., University of New South Wales (2012) 
15. Ruan, J., van der Hoek, W., Wooldridge, M.: Verification of games in the game description language. Journal Logic and Computation 19(6), 1127–1156 (2009) 
16. Ruan, J., Thielscher, M.: The epistemic logic behind the game description language. In: Pro- ceedings of AAAI, San Francisco, pp. 840–845 (2011) 
17. Ruan, J., Thielscher, M.: Model checking games in GDL-II: the technical report. Tech. Rep.CSE-TR-201219, University of New South Wales (2012) 
18. Ruan, J., Thielscher, M.: Strategic and epistemic reasoning for the game description language GDL-II. In: Proceedings of ECAI, Montpellier, pp. 696–701 (2012) 258 X. Huang, J. Ruan, and M. Thielscher 
19. Schiffel, S., Thielscher, M.: Fluxplayer: A successful general game player. In: Proceedings of AAAI, pp. 1191–1196. AAAI Press (2007) 
20. Thielscher, M.: A general game description language for incomplete information games. In: Proceedings of AAAI, pp. 994–999 (2010) 
21. Thielscher, M.: The general game playing description language is universal. In: Proceedings of IJCAI, Barcelona, pp. 1107–1112 (2011) 
22. Whaley, J., Avots, D., Carbin, M., Lam, M.S.: Using datalog with binary decision diagrams for program analysis. In: Yi, K. (ed.) APLAS 2005. LNCS, vol. 3780, pp. 97–118. Springer, Heidelberg (2005) 
-----2
1. Laird, J., VanLent, M.: Human-level AI’s killer application: Interactive computer games. AI Magazine 22(2), 15–26 (2001) 
2. Buro, M.: Call for AI research in RTS games. In: Proceedings of the AAAI 2004 Workshop on Challenges in Game AI, pp. 2–4 (2004) 
3. Siwek, S.E.: Video Games in the 21st Century. Technical report. Entertainment Software Association (2010) 
4. Yildirim, S., Stene, S.B.: A survey on the need and use of ai in game agents. In: Proceedings of the 2008 Spring Simulation Multiconference, pp. 124–131 (2008) 
5. Mehta, M., Ontan˜o´n, S., Amundsen, T., Ram, A.: Authoring behaviors for games using learning from demonstration. In: Workshop on Case-Based Reasoning for Computer Games, ICCBR (2009) 
6. Olesen, J.K., Yannakakis, G.N., Hallam, J.: Real-time challenge balance in an RTS game using rtNEAT. In: 2008 IEEE Symposium on Computational Intelligence and Games, pp. 87–94 (2008) 
7. Buro, M., Furtak, T.M.: RTS games and real-time AI research. In: Proceedings of the Behavior Representation in Modeling and Simulation Conference, pp. 63–70 (2004) 
8. Stanley, K.O., Miikkulainen, R.: Efficient Evolution of Neural Network Topologies.In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002).IEEE (2002) 
9. Wender, S., Watson, I.: Applying reinforcement learning to small scale combat in the real-time strategy game StarCraft:Broodwar. In: Computational Intelligence and Games (CIG), pp. 402–408 (2012) 
10. Shantia, A., Begue, E., Wiering, M.: Connectionist reinforcement learning for in- telligent unit micro management in starcraft. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1794–1801 (2011) 
11. Cadena, P., Garrido, L.: Fuzzy Case-Based Reasoning for Managing Strategic and Tactical Reasoning in StarCraft. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part I. LNCS, vol. 7094, pp. 113–124. Springer, Heidelberg (2011) 
12. Weber, B., Mateas, M., Jhala, A.: Applying goal-driven autonomy to StarCraft. In: Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010 (2010) 
13. Davis, I.L.: Strategies for strategy game AI. In: Proceedings of the AAAI Spring Symposium on Artificial Intelligence and Computer Games, pp. 24–27 (1999) 
14. Gabriel, I., Negru, V., Zaharie, D.: Neuroevolution based multi-agent system for micromanagement in real-time strategy games. In: Proceedings of the Fifth Balkan Conference in Informatics - BCI 2012, p. 32 (2012) 
15. Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87, 1423–1447 (1999) 
16. Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002) 
17. Stanley, K.O.: Evolving neural network agents in the NERO video game. In: Pro- ceedings of the IEEE 2005 Symposium on Computational Intelligence and Games, pp. 182–189 (2005) 
18. Jang, S.H., Yoon, J.W., Cho, S.B.: Optimal strategy selection of non-player char- acter on real time strategy game using a speciated evolutionary algorithm. In: Proceedings of the 5th International Conference on Computational Intelligence and Games, pp. 75–79 (2009) 
-----2
1. Barbu, A., Narayanaswamy, S., Siskind, J.: Learning physically-instantiated game play through visual observation. In: Proc. of ICRA, pp. 1879–1886. IEEE Press (2010) 
2. Bjo¨rnsson, Y., Finnsson, H.: CADIAPLAYER: A simulation-based general game player.IEEE Transactions on Computational Intelligence and AI in Games 1(1), 4–15 (2009) 
3. Broekens, J., Heerink, M., Rosendal, H.: Assistive social robots in elderly care: a review.Gerontechnology 8(2) (2009) 
4. Clune, J.: Heuristic evaluation functions for general game playing. In: Proc. of AAAI, pp.1134–1139 (2007) 
5. Genesereth, M., Love, N., Pell, B.: General game playing: Overview of the AAAI competi- tion. AI Magazine 26(2), 62–72 (2005) 
6. Goldfeder, C., Ciocarlie, M.T., Dang, H., Allen, P.K.: The columbia grasp database. In: Proc.of ICRA, pp. 1710–1716. IEEE Press (2009) 
7. Haufe, S., Schiffel, S., Thielscher, M.: Automated verification of state sequence invariants in general game playing. Artificial Intelligence 187-188, 1–30 (2012) 
8. Kaiser, ?.: Learning games from videos guided by descriptive complexity. In: Proc. of AAAI, pp. 963–969 (2012) 
9. Kemp, C.C., Edsinger, A., Torres-Jara, E.: Challenges for robot manipulation in human en- vironments. IEEE Robotics & Automation Magazine 14(1), 20–29 (2007) 
10. Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view rgb-d object dataset.In: Proc. of ICRA, pp. 1817–1824. IEEE Press (2011) 
11. Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009) 
12. Schiffel, S., Thielscher, M.: Fluxplayer: A successful general game player. In: Proc. of AAAI, pp. 1191–1196 (2007) 
13. Schiffel, S., Thielscher, M.: A Multiagent Semantics for the Game Description Language.In: Filipe, J., Fred, A., Sharp, B. (eds.) ICAART 2009. CCIS, vol. 67, pp. 44–55. Springer, Heidelberg (2010) 
-----2
1. Andersen, M.B., Bolander, T., Jensen, M.H.: Conditional epistemic planning. In: del Cerro, L.F., Herzig, A., Mengin, J. (eds.) JELIA 2012. LNCS, vol. 7519, pp.94–106. Springer, Heidelberg (2012) 
2. Andersen, M.B., Bolander, T., Jensen, M.H.: Don’t plan for the unexpected: Plan- ning based on plausibility models. Logique et Analyse (to appear, 2014) 
3. Aucher, G.: A combined system for update logic and belief revision. In: Bar- ley, M.W., Kasabov, N. (eds.) PRIMA 2004. LNCS (LNAI), vol. 3371, pp. 1–17.Springer, Heidelberg (2005) 
4. Aucher, G.: DEL-sequents for regression and epistemic planning. Journal of Ap- plied Non-Classical Logics 22(4), 337–367 (2012) 
5. Baltag, A., Smets, S.: A qualitative theory of dynamic interactive belief revision.In: Proc. of 7th LOFT. Texts in Logic and Games, vol. 3, pp. 13–60. Amsterdam University Press (2008) 
6. Bolander, T., Andersen, M.B.: Epistemic planning for single and multi-agent sys- tems. Journal of Applied Non-classical Logics 21(1), 9–34 (2011) 
7. Britz, K., Varzinczak, I.: Defeasible modalities. In: Proc. of the 14th TARK (2013) 
8. Demey, L.: Some remarks on the model theory of epistemic plausibility models.Journal of Applied Non-Classical Logics 21(3-4), 375–395 (2011) 
9. Grove, A.: Two modellings for theory change. Journal of Philosophical Logic 17, 157–170 (1988) 
10. Kraus, S., Lehmann, D., Magidor, M.: Nonmonotonic reasoning, preferential mod- els and cumulative logics. Artificial Intelligence 44, 167–207 (1990) 
11. Lewis, D.K.: Counterfactuals. Harvard University Press, Cambridge (1973) 
12. Lo¨we, B., Pacuit, E., Witzel, A.: DEL planning and some tractable cases. In: van Ditmarsch, H., Lang, J., Ju, S. (eds.) LORI 2011. LNCS, vol. 6953, pp. 179–192.Springer, Heidelberg (2011) 
13. Meyer, T.A., Labuschagne, W.A., Heidema, J.: Refined epistemic entrenchment.Journal of Logic, Language, and Information 9, 237–259 (2000) 
14. Segerberg, K.: Irrevocable belief revision in dynamic doxastic logic. Notre Dame Journal of Formal Logic 39(3), 287–306 (1998) 
15. Spohn, W.: Ordinal conditional functions: a dynamic theory of epistemic states.In: Harper, W.L., Skyrms, B. (eds.) Causation in Decision, Belief Change, and Statistics, vol. II, pp. 105–134 (1988) 
16. Stalnaker, R.: Knowledge, belief and counterfactual reasoning in games. Economics and Philosophy 12, 133–163 (1996) 
17. van Benthem, J.: Dynamic logic of belief revision. Journal of Applied Non-Classical Logics 17(2), 129–155 (2007) 
18. van Benthem, J.: Logical Dynamics of Information and Interaction. Cambridge University Press (2011) 
19. van Ditmarsch, H.: Prolegomena to dynamic logic for belief revision. Synthese (Knowledge, Rationality & Action) 147, 229–275 (2005) 
20. van Ditmarsch, H., Labuschagne, W.A.: My beliefs about your beliefs – a case study in theory of mind and epistemic logic. Synthese 155, 191–209 (2007) 
-----2
1. Alur, R., Dill, D.L.: A theory of timed automata. Theoretical Computer Sci- ence 126, 183–235 (1994) 
2. Bian, J.: Efficient tableaux for temporal logic. Doctoral thesis, in preparation (2013), http://www.csse.uwa.edu.au/~jibian/thesis/thesis.pdf 
3. Bian, J.: Linear time tableaux for partial mosaic, Online solver (2013), http://www.csse.uwa.edu.au/~jibian/partialmosaictab/ 
4. Burgess, J.: Axioms for tense logic I: ‘Since’ and ‘Until’. Notre Dame J. Formal Logic 23(2), 367–374 (1994) 
5. Burgess, J., Gurevich, Y.: The decision problem for linear temporal logic. Notre Dame J. Formal Logic 26(2), 115–128 (1985) 
6. Hirsch, R., Hodkinson, I., Marx, M., Mikulas, S., Reynolds, M.: Mosaics and step- by-step. Remarks on “A modal logic of relations”. In: Orlowska, E. (ed.) Logic at Work: Essays Dedicated to the Memory of Helen Rasiowa. STUDFUZZ, vol. 24, pp. 158–167. Springer, Heidelberg (1999) 
7. Kamp, H.: Tense logic and the theory of linear order. PhD thesis, University of California (1968) 
8. Kesten, Y., Manna, Z., Pnueli, A.: Temporal verification of simulation and refine- ment. In: de Bakker, J.W., de Roever, W.-P., Rozenberg, G. (eds.) REX 1993.LNCS, vol. 803, pp. 273–346. Springer, Heidelberg (1994) 
9. Marx, M., Mikulas, S., Reynolds, M.: The mosaic method for temporal logics.In: Dyckhoff, R. (ed.) TABLEAUX 2000. LNCS (LNAI), vol. 1847, pp. 324–340.Springer, Heidelberg (2000) 
10. Pnueli, A.: The temporal logic of programs. In: Proceedings of the Eighteenth Symposium on Foundations of Computer Science, pp. 46–57. Springer (1977) 
11. Reynolds, M.: Dense time reasoning via mosaics. In: TIME 2009: Proceedings of the 2009 16th International Symposium on Temporal Representation and Reasoning, pp. 3–10. IEEE Computer Society (2009) 
12. Reynolds, M.: The complexity of temporal logics over linear time. Journal of Stud- ies in Logic 3, 19–50 (2010) 
13. Reynolds, M.: A tableau for until and since over linear time. In: Proc. of 18th In- ternational Symposium on Temporal Representation and Reasoning (TIME 2011), Lubeck, Germany. IEEE Computer Society Press (September 2011) 
14. Sistla, A., Clarke, E.: Complexity of propositional linear temporal logics. J.ACM 32, 733–749 (1985) 
15. Yin, Z., Tambe, M.: Continuous time planning for multiagent teams with temporal constraints. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011, pp. 465–471 (2011) 
-----2
1. Bigot, D., Fargier, H., Mengin, J., Zanuttini, B.: Probabilistic conditional preference net- works. In: Proc. 29th Conf. on Uncertainty in Artificial Intelligence, UAI (2013) 
2. Boutilier, C., Brafman, R., Domshlak, C., Hoos, H., Poole, D.: CP-nets: A tool for represent- ing and reasoning with conditional ceteris paribus preference statements. Journal of Artificial Intelligence Research 21, 135–191 (2004) 
3. Boutilier, C.: A POMDP formulation of preference elicitation problems. In: Proc. 18th AAAI Conference on Artificial Intelligence, pp. 239–246 (2002) 
4. D’Ambrosio, B.: Inference in Bayesian Networks. AI Magazine 20(2), 21 (1999) 
5. Dechter, R.: Bucket elimination: A unifying framework for reasoning. Artificial Intelli- gence 113(1-2), 41–85 (1999) 
6. Domshlak, C., Brafman, R.: CP-nets: Reasoning and consistency testing. In: Proc. 8th Intl.Conf. on Principles and Knowledge Representation and Reasoning, KRR (2002) 
7. Faltings, B., Torrens, M., Pu, P.: Solution generation with qualitative models of preferences.Computational Intelligence 20(2), 246–263 (2004) 
8. Fu¨rnkranz, J., Hu¨llermeier, E.: Preference Learning: An Introduction. Springer (2010) 
9. Goldsmith, J., Junker, U.: Preference handling for artificial intelligence. AI Magazine 29(4) (2009) 
10. Goldsmith, J., Lang, J., Truszczynski, M., Wilson, N.: The computational complexity of dominance and consistency in CP-nets. Journal of Artificial Intelligence Research 33(1), 403–432 (2008) 
11. Maran, A., Maudet, N., Pini, M.S., Rossi, F., Venable, K.B.: A framework for aggregating influenced CP-nets and its resistance to bribery. In: Proc. 27th AAAI Conference on Artificial Intelligence (2013) 
12. Marden, J.I.: Analyzing and Modeling Rank Data. CRC Press (1995) 
13. Mattei, N., Pini, M.S., Rossi, F., Venable, K.B.: Bribery in voting over combinatorial domains is easy. In: Proc. 11th Intl. Joint Conf. on Autonomous Agents and Multiagent Systems, AAMAS (2012) 
14. Mattei, N., Pini, M.S., Rossi, F., Venable, K.B.: Bribery in voting with CP-nets. Annals of Mathematics and Artificial Intelligence (2013) 
15. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.Morgan Kaufmann (1988) 
16. Price, R., Messinger, P.R.: Optimal recommendation sets: Covering uncertainty over user preferences. In: Proc. 20th AAAI Conference on Artificial Intelligence, pp. 541–548 (2005) 
17. Regenwetter, M., Dana, J., Davis-Stober, C.P.: Transitivity of preferences. Psychological Re- view 118(1) (2011) 
18. Rossi, F., Venable, K., Walsh, T.: mCP nets: representing and reasoning with preferences of multiple agents. In: Proc. 19th AAAI Conference on Artificial Intelligence, pp. 729–734 (2004) 
19. Roth, A.E., Kagel, J.H.: The handbook of experimental economics, vol. 1. Princeton Univer- sity Press, Princeton (1995) 
20. Tversky, A., Kahneman, D.: Judgement under uncertainty: Heuristics and biases. Sci- ence 185, 1124–1131 (1974) 
21. Xia, L., Conitzer, V., Lang, J.: Voting on multiattribute domains with cyclic preferential de- pendencies. In: Proc. 23rd AAAI Conference on Artificial Intelligence, pp. 202–207 (2008) 
-----2
1. Bartholdi, J., Tovey, C.A., Trick, M.A.: Voting schemes for which it can be difficult to tell who won the election. Social Choice and Welfare 6, 157–165 (1989) 
2. Baumeister, D., Erde´lyi, G., Erde´lyi, O.J., Rothe, J.: Control in judgment aggre- gation. In: Proceedings of the 6th European Starting AI Researcher Symposium, pp. 23–34 (2012) 
3. Baumeister, D., Erde´lyi, G., Erde´lyi, O.J., Rothe, J.: Computational aspects of manipulation and control in judgment aggregation. In: Proceedings of the 3rd In- ternational Conference on Algorithmic Decision Theory, ADT 2013 (forthcomming, 2013) 
4. Baumeister, D., Erde´lyi, G., Rothe, J.: How hard is it to bribe the judges? A study of the complexity of bribery in judgment aggregation. In: Brafman, R., Roberts, F.S., Tsoukia`s, A. (eds.) ADT 2011. LNCS, vol. 6992, pp. 1–15. Springer, Heidel- berg (2011) 
5. Bulling, N., Farwer, B.: On the (un-)decidability of model checking resource- bounded agents. In: Proceedings of ECAI. Frontiers in Artificial Intelligence and Applications, vol. 215, pp. 567–572. IOS Press (2010) 
6. Chevaleyre, Y., Endriss, U., Lang, J., Maudet, N.: A short introduction to com- putational social choice. In: van Leeuwen, J., Italiano, G.F., van der Hoek, W., Meinel, C., Sack, H., Pla´s?il, F. (eds.) SOFSEM 2007. LNCS, vol. 4362, pp. 51–69.Springer, Heidelberg (2007) 
7. Conitzer, V., Sandholm, T.: Complexity of manipulating elections with few candi- dates. In: Proceedings of AAAI 2002, pp. 314–319. American Association of Arti- ficial Intelligence (2002) 
8. Conitzer, V., Sandholm, T.: Communication complexity of common voting rules.In: Proceedings of the ACM Conference on Electronic Commerce, pp. 78–87 (2005) 
9. Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping.The Annals of Mathematical Statistics 38(2) (1967) 
10. Deza, M.M., Deza, E.: Encyclopedia of Distances. Springer (2009) 324 W. Jamroga and M. Slavkovik 
11. Dietrich, F.: A generalised model of judgment aggregation. Social Choice and Wel- fare 28(4), 529–565 (2007) 
12. Dietrich, F.: Judgment aggregation by quota rules majority voting generalized.Journal of Theoretical Politics 19(4), 391–424 (2007) 
13. Dietrich, F.: Scoring rules for judgment aggregation. Social Choice and Welfare (to appear, 2013) 
14. Dietrich, F., Mongin, P.: The premiss-based approach to judgment aggregation.Journal of Economic Theory 145(2), 562–582 (2010) 
15. Dima, C., Tiplea, F.L.: Model-checking atl under imperfect information and perfect recall semantics is undecidable. CoRR, abs/1102.4225 (2011) 
16. Dokow, E., Holzman, R.: Aggregation of binary evaluations with abstentions. Jour- nal of Economic Theory 145(2), 544–561 (2010) 
17. Duddy, C., Piggins, A.: Many-valued judgment aggregation: Characterizing the possibility/impossibility boundary. Journal of Economic Theory 148(2), 793–805 (2013) 
18. Eckert, D., Klamler, C., Mitlo¨hner, J., Schlo¨tterer, C.: A distance-based compari- son of basic voting rules. Central European Journal of Operations Research 14(4), 377–386 (2006) 
19. Eckert, D., Mitlo¨hner, J.: Logical representation and merging of preference informa- tion. In: Proceedings of the IJCAI 2005 Multidisciplinary Workshop on Preference Handling (2005) 
20. Endriss, U., Grandi, U., Porello, D.: Complexity of judgment aggregation: Safety of the agenda. In: Proceedings of AAMAS 2010, pp. 359–366 (2010) 
21. Endriss, U., Grandi, U., Porello, D.: Complexity of winner determination and strategic manipulation in judgment aggregation. In: Proceedings of COMSOC 2010 (2010) 
22. Endriss, U., Grandi, U., Porello, D.: Complexity of judgment aggregation. Journal of Artificial Intelligence Research 45, 481–514 (2012) 
23. Escoffier, B., Lang, J., O¨ztu¨rk, M.: Single-peaked consistency and its complexity.In: Proceedings of ECAI 2008, pp. 366–370. IOS Press (2008) 
24. Fagin, R., Halpern, J.Y., Moses, Y., Vardi, M.Y.: Reasoning about Knowledge.MIT Press (1995) 
25. Grabisch, M., Marichal, J.-L., Mesiar, R., Pap, E.: Aggregation Functions. Cam- bridge University Press (2009) 
26. Grossi, D.: Correspondences in the theory of aggregation. In: Bonanno, G., Lo¨we, B., van der Hoek, W. (eds.) LOFT 2008. LNCS, vol. 6006, pp. 34–60. Springer, Heidelberg (2010) 
27. Hemaspaandra, E., Spakowski, H., Vogel, J.: The complexity of kemeny elections.Theoretical Computer Science 349(3), 382–391 (2005) 
28. Konieczny, S., Lang, J., Marquis, P.: Distance-based merging: A general framework and some complexity results. In: Proceedings of KR 2002, pp. 97–108 (2002) 
29. Lang, J., Pigozzi, G., Slavkovik, M., van der Torre, L.: Judgment aggregation rules based on minimization. In: Proceedings of TARK, pp. 238–246 (2011) 
30. Lang, J., Slavkovik, M.: Judgment aggregation rules and voting rules. In: Perny, P., Pirlot, M., Tsoukia´s, A. (eds.) ADT 2013. LNCS, vol. 8176, pp. 230–243. Springer, Heidelberg (2013) 
31. Li, N.: Decision paths in sequential non-binary judgment aggregation. Technical report, Universitat Auto`noma de Barcelona (2010) 
32. List, C., Pettit, P.: Aggregating sets of judgments: An impossibility result. Eco- nomics and Philosophy 18(01), 89–110 (2002) Some Complexity Results for Distance-Based Judgment Aggregation 325 
33. List, C., Polak, B.: Introduction to judgment aggregation. Journal of Economic Theory 145(2), 441–466 (2010) 
34. Miller, M.K., Osherson, D.: Methods for distance-based judgment aggregation.Social Choice and Welfare 32(4), 575–601 (2009) 
35. Nehring, K., Pivato, M.: Incoherent majorities: The McGarvey problem in judge- ment aggregation. Discrete Applied Mathematics 159(15), 1488–1507 (2011) 
36. Pauly, M., van Hees, M.: Logical constraints on judgement aggregation. Journal of Philosophical Logic 35(6), 569–585 (2006) 
37. Peterson, G., Reif, J., Azhar, S.: Lower bounds for multiplayer noncooperative games of incomplete information. Computers and Mathematics with Applica- tions 41(7), 957–992 
38. Pigozzi, G.: Belief merging and the discursive dilemma: an argument-based account to paradoxes of judgment aggregation. Synthese 152(2), 285–298 (2006) 
39. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press (1976) 
40. Slavkovik, M., Jamroga, W.: Distance-based judgment aggregation of three-valued judgments with weights. In: Proceedings of the IJCAI Workshop on Social Choice and Artificial Intelligence 
41. van der Hoek, W., Verbrugge, R.: Epistemic logic: A survey. Game Theory and Applications 8, 53–94 (2002) 
42. Wilson, R.: On the theory of aggregation. Journal of Economic Theory 10(1), 89–99 (1975) 
-----2
1. Britz, K., Heidema, J., Labuschagne, W.: Semantics for dual preferential entail- ment. Journal of Philosophical Logic 38, 433–446 (2009) 
2. Britz, K., Heidema, J., Varzinczak, I.: Constrained consequence. Logica Univer- salis 5, 327–350 (2011) 
3. Freund, M.: On the notion of concept I. Artificial Intelligence 172, 570–590 (2008) 
4. Freund, M.: On the notion of concept II. Artificial Intelligence 173, 167–179 (2009) 
5. Hauser, N., Kloesel, C.: The Essential Peirce, vol. 1, pp. 1867–1893. Indiana Uni- versity Press, Bloomington (1992) 
6. Hauser, N., Kloesel, C.: The Essential Peirce, vol. 2, pp. 1893–1913. Indiana Uni- versity Press, Bloomington (1998) 
7. Johnson-Laird, P.: Mental Models. Harvard University Press, Cambridge (1983) 
8. Kraus, S., Lehmann, D., Magidor, M.: Nonmonotonic reasoning, preferential mod- els and cumulative logics. Artificial Intelligence 44, 167–207 (1990) 
9. Lehmann, D., Magidor, M.: What does a conditional knowledge base entail? Arti- ficial Intelligence 55, 1–60 (1992) 
10. Rodrigues, C.T.: The method of scientific discovery in Peirce’s philosophy: Deduc- tion, induction, and abduction. Logica Universalis 5, 127–164 (2011) 
11. Shoham, Y.: Reasoning about change: Time and causation from the standpoint of artificial intelligence. The MIT Press, Cambridge (1988) 
12. van Benthem, J.: Logic and reasoning: Do the facts matter? Studia Logica 88, 67–84 (2008) 
13. Zhang, D., et al.: Nonmonotonic reasoning and multiple belief revision. In: Pro- ceedings IJCAI 1997, vol. 1, pp. 95–100. Morgan Kaufmann, Los Altos (1997) 
-----2
1. Antoniou, G., Billington, D., Governatori, G., Maher, M.J.: On the modelling and analysis of regulations. In: Proc. Australasian Conf. on Information Systems, pp. 20–29 (1999) 
2. Antoniou, G., Billington, D., Governatori, G., Maher, M.J.: Representation results for defea- sible logic. ACM Trans. Comput. Log. 2(2), 255–287 (2001) 
3. Antoniou, G., Billington, D., Governatori, G., Maher, M.J.: Embedding defeasible logic into logic programming. TPLP 6(6), 703–735 (2006) 
4. Bassiliades, N., Antoniou, G., Vlahavas, I.P.: A defeasible logic reasoner for the semantic web. Int. J. Semantic Web Inf. Syst. 2(1), 1–41 (2006) 
5. Billington, D., Antoniou, G., Governatori, G., Maher, M.J.: An inclusion theorem for defea- sible logics. ACM Trans. Comput. 12(1), 6 (2010) 
6. Dumas, M., Governatori, G., ter Hofstede, A.H.M., Oaks, P.: A formal approach to negoti- ating agents development. Electronic Commerce Research and Applications 1(2), 193–207 (2002) 
7. Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–358 (1995) 
8. Gelder, A.V., Ross, K.A., Schlipf, J.S.: The well-founded semantics for general logic pro- grams. J. ACM 38(3), 620–650 (1991) 
9. Governatori, G.: Representing business contracts in ruleml. Int. J. Cooperative Inf.Syst. 14(2-3), 181–216 (2005) 
10. Governatori, G., Rotolo, A.: Changing legal systems: legal abrogations and annulments in defeasible logic. Logic Journal of the IGPL 18(1), 157–194 (2010) 
11. Governatori, G., Rotolo, A., Padmanabhan, V.: The cost of social agents. In: Nakashima, H., Wellman, M.P., Weiss, G., Stone, P. (eds.) AAMAS, pp. 513–520. ACM (2006) 
12. Grosof, B.N.: Prioritized conflict handling for logic programs. In: ILPS, pp. 197–211 (1997) 
13. Grosof, B.N., Labrou, Y., Chan, H.Y.: A declarative approach to business rules in contracts: courteous logic programs in XML. In: ACM Conference on Electronic Commerce, pp. 68–77 (1999) 
14. Maher, M.J.: Relative expressiveness of defeasible logics. TPLP 12(4-5), 793–810 (2012) 
15. Maher, M.J.: Relative expressiveness of defeasible logics II. TPLP 13(4-5), 579–592 (2013) 
16. Maher, M.J., Governatori, G.: A semantic decomposition of defeasible logics. In: AAAI/IAAI, pp. 299–305. AAAI Press (1999) 
17. Maier, F., Nute, D.: Ambiguity propagating defeasible logic and the well-founded semantics.In: Fisher, M., van der Hoek, W., Konev, B., Lisitsa, A. (eds.) JELIA 2006. LNCS (LNAI), vol. 4160, pp. 306–318. Springer, Heidelberg (2006) 
18. Maier, F., Nute, D.: Well-founded semantics for defeasible logic. Synthese 176(2), 243–274 (2010) 
19. Nute, D.: Defeasible logic. In: Bartenstein, O., Geske, U., Hannebauer, M., Yoshie, O. (eds.) INAP 2001. LNCS (LNAI), vol. 2543, pp. 151–169. Springer, Heidelberg (2003) 
20. Prakken, H.: Logical Tools for Modelling Legal Argument: A Study of Defeasible Reasoning in Law. Kluwer (1997) 
21. Rahwan, I., Simari, G.: Argumentation in Artificial Intelligence. Springer (2009) 
22. Reeves, D.M., Wellman, M.P., Grosof, B.N.: Automated negotiation from declarative con- tract descriptions. Computational Intelligence 18(4), 482–500 (2002) 
-----2
1. Artale, A., Calvanese, D., Kontchakov, R., Zakharyaschev, M.: The DL-Lite family and re- lations. J. Artif. Intell. Res (JAIR) 36, 1–69 (2009) 
2. Baader, F., Brandt, S., Lutz, C.: Pushing the EL Envelope. In: Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 364–369 (2005) 
3. Borgida, A., Weddell, G.: Adding Uniqueness Constraints to Description Logics (Prelimi- nary Report). In: Bry, F., Ramakrishnan, R., Ramamohanarao, K. (eds.) DOOD 1997. LNCS, vol. 1341, pp. 85–102. Springer, Heidelberg (1997) Conjunctive Query Answering in CFDnc 361 
4. Calvanese, D., de Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Tractable Reason- ing and Efficient Query Answering in Description Logics: The DL-Lite Family. Journal of Automated Reasoning 39(3), 385–429 (2007) 
5. Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Path-Based Identifi- cation Constraints in Description Logics. In: Proc. of the 11th Int. Joint Conf. on Principles of Knowledge Representation and Reasoning (KR), pp. 231–241 (2008) 
6. Calvanese, D., De Giacomo, G., Lenzerini, M.: Identification Constraints and Functional De- pendencies in Description Logics. In: Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 155–160 (2001) 
7. Hopcroft, J.E., Ullman, J.D.: Introduction to Automata Theory, Languages and Computation.Addison-Wesley (1979) 
8. Ito, M., Weddell, G.: Implication Problems for Functional Constraints on Databases Support- ing Complex Objects. Journal of Computer and System Sciences 49(3), 726–768 (1994) 
9. Khizder, V.L., Toman, D., Weddell, G.: On Decidability and Complexity of Description Log- ics with Uniqueness Constraints. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 54–67. Springer, Heidelberg (2000) 
10. Kontchakov, R., Lutz, C., Toman, D., Wolter, F., Zakharyaschev, M.: The combined approach to query answering in DL-Lite. In: KR (2010) 
11. Kozen, D.: Lower bounds for natural proof systems. In: Proceedings of the 18th Annual Symposium on Foundations of Computer Science, pp. 254–266. IEEE Computer Society (1977) 
12. Lutz, C., Toman, D., Wolter, F.: Conjunctive query answering in the description logic EL using a relational database system. In: Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 2070–2075 (2009) 
13. Toman, D., Weddell, G.: On Reasoning about Structural Equality in XML: A Description Logic Approach. Theoretical Computer Science 336(1), 181–203 (2005) 
14. Toman, D., Weddell, G.: On the Interaction between Inverse Features and Path-functional Dependencies in Description Logics. In: Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 603–608 (2005) 
15. Toman, D., Weddell, G.: On Keys and Functional Dependencies as First-Class Citizens in De- scription Logics. In: Furbach, U., Shankar, N. (eds.) IJCAR 2006. LNCS (LNAI), vol. 4130, pp. 647–661. Springer, Heidelberg (2006) 
16. Toman, D., Weddell, G.E.: On keys and functional dependencies as first-class citizens in description logics. J. Autom. Reasoning 40(2-3), 117–132 (2008) 
17. Toman, D., Weddell, G.E.: Applications and extensions of ptime description logics with func- tional constraints. In: Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 948–954 (2009) 
18. Weddell, G.: A Theory of Functional Dependencies for Object Oriented Data Models. In: International Conference on Deductive and Object-Oriented Databases, pp. 165–184 (1989) 
-----2
1. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Proc. Conf. on Neural Information Processing Sys- tems, pp. 561–568. MIT Press (2003) 
2. Bjerring, L., Frank, E.: Beyond trees: Adopting MITI to learn rules and ensemble classifiers for multi-instance data. In: Wang, D., Reynolds, M. (eds.) AI 2011.LNCS, vol. 7106, pp. 41–50. Springer, Heidelberg (2011) Propositionalisation of Multi-instance Data Using Random Forests 373 
3. Blockeel, H., Page, D., Srinivasan, A.: Multi-instance tree learning. In: Proc. 22nd Int. Conf. on Machine Learning, pp. 57–64. ACM (2005) 
4. Braddock, P.S., Hu, D.E., Fan, T.P., Stratford, I., Harris, A.L., Bicknell, R.: A structure-activity analysis of antagonism of the growth factor and angiogenic ac- tivity of basic fibroblast growth factor by suramin and related polyanions. Br. J.Cancer 69(5), 890–898 (1994) 
5. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001) 
6. Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89(1-2), 31–71 (1997) 
7. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach.Learn. 63(1), 3–42 (2006) 
8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009) 
9. Kramer, S., Lavrac?, N., Flach, P.: Propositionalization approaches to relational data mining. In: Relational Data Mining, pp. 262–286. Springer (2000) 
10. Krogel, M.-A., Rawles, S., Z?elezny´, F., Flach, P.A., Lavrac?, N., Wrobel, S.: Compar- ative evaluation of approaches to propositionalization. In: Horva´th, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 197–214. Springer, Heidelberg (2003) 
11. Maron, O., Lozano-Pe´rez, T.: A framework for multiple-instance learning. In: Proc.Conf. on Neural Information Processing Systems, pp. 570–576. MIT Press (1998) 
12. Mayo, M.: Effective classifiers for detecting objects. In: Proc. 4th Int. Conf. on Computational Intelligence, Robotics, and Autonomous Systems (2007) 
13. Nadeau, C., Bengio, Y.: Inference for the Generalization Error. Machine Learn- ing 52(3), 239–281 (2003) 
14. Opelt, A., Pinz, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. IEEE Transaction on Pattern Analysis and Machine Intelligence 28(3), 416–431 (2006) 
15. Reutemann, P., Pfahringer, B., Frank, E.: A toolbox for learning from relational data with propositional and multi-instance learners. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 1017–1023. Springer, Heidelberg (2004) 
16. Srinivasan, A., Muggleton, S., King, R., Sternberg, M.: Mutagenesis: ILP exper- iments in a non-determinate biological domain. In: Proc. 4th Int Workshop on Inductive Logic Programming, pp. 217–232. GMD (1994) 
17. Wang, C., Scott, S., Zhang, J., Tao, Q., Fomenko, D., Gladyshev, V.: A study in modeling low-conservation protein superfamilies. Tech. rep., Department of Comp.Sci., University of Nebraska-Lincoln (2004) 
18. Weidmann, N., Frank, E., Pfahringer, B.: A two-level learning method for gen- eralized multi-instance problems. In: Lavrac?, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 468–479. Springer, Heidelberg (2003) 
19. Chevaleyre, Y., Zucker, J.-D.: Solving multiple-instance and multiple-part learning problems with decision trees and rule sets. Application to the mutagenesis problem.In: Stroulia, E., Matwin, S. (eds.) AI 2001. LNCS (LNAI), vol. 2056, pp. 204–214.Springer, Heidelberg (2001) 
-----2
1. Yang, Q., Wu, X.: 10 challenging problems in data mining research. International Journal of Information Technology & Decision Making 5(4), 597–604 (2006) 
2. Hoens, T.R., Qian, Q., Chawla, N.V., Zhou, Z.-H.: Building decision trees for the multi-class imbalance problem. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J.(eds.) PAKDD 2012, Part I. LNCS, vol. 7301, pp. 122–134. Springer, Heidelberg (2012) 
3. Liang, G., Zhang, C.: An efficient and simple under-sampling technique for imbalanced time series classification. In: CIKM 2012, pp. 2339–2342 (2012) 
4. Hidasi, B., Ga´spa´r-Papanek, C.: ShiftTree: An interpretable model-based approach for time series classification. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 48–64.Springer, Heidelberg (2011) 
5. Liang, G., Zhang, C.: A comparative study of sampling methods and algorithms for imbalanced time series classification. In: Thielscher, M., Zhang, D. (eds.) AI 2012. LNCS, vol. 7691, pp. 637–648. Springer, Heidelberg (2012) 
6. Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996) An Effective Method for Imbalanced Time Series Classification 385 
7. Quinlan, J.: Bagging, boosting, and c4.5. In: Proceedings of the 13th National Conference on Artificial Intelligence, pp. 725–730 (1996) 
8. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36(1), 105–139 (1999) 
9. Dietterich, T.: An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 40(2), 139–157 (2000) 
10. Cao, H., Li, X., Woon, Y., Ng, S.: SPO: Structure preserving oversampling for imbalanced time series classification. In: Proceedings of the IEEE 11th International Conference on Data Mining, ICDM 2011, pp. 1008–1013 (2011) 
11. Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16(1), 321–357 (2002) 
12. Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878–887. Springer, Heidelberg (2005) 
13. He, H., Bai, Y., Garcia, E., Li, S.: ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In: IEEE International Joint Conference on Neural Networks, IJCNN 2008, pp. 1322–1328. IEEE (2008) 
14. Guo, H., Viktor, H.L.: Learning from imbalanced data sets with boosting and data generation: The DataBoost-IM approach. ACM SIGKDD Explorations Newsletter 6(1), 30–39 (2004) 
15. Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(2), 539–550 (2009) 
16. Wei, L., Keogh, E.: Semi-supervised time series classification. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 748–753. ACM (2006) 
17. Xi, X., Keogh, E., Shelton, C., Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 1033–1040 (2006) 
18. Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tool and Techniques. Morgan Kaufmann (2005) 
19. Dems?ar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006) 
20. Liang, G.: An investigation of sensitivity on bagging predictors: An empirical approach. In: 26th AAAI Conference on Artificial Intelligence, pp. 2439–2440 (2012) 
21. Liang, G., Zhu, X., Zhang, C.: The effect of varying levels of class distribution on bagging with different algorithms: An empirical study. International Journal of Machine Learning and Cybernetics (2012), http://link.springer.com/article/10.1007%2Fs13042--012--0125--5 
22. Liang, G., Zhang, C.: Empirical study of bagging predictors on medical data. In: 9th Australian Data Mining Conference, AusDM 2011, pp. 31–40 (2011) 
23. Keogh, E., Zhu, Q., Hu, B., Hao, Y., Xi, X., Wei, L., Ratanamahatana, C.A.: The UCR Time Series Classification/Clustering homepage (2011), http://www.cs.ucr.edu/~eamonn/time_series_data/ 
24. Liang, G., Zhu, X., Zhang, C.: An empirical study of bagging predictors for imbalanced data with different levels of class distribution. In: Wang, D., Reynolds, M. (eds.) AI 2011. LNCS, vol. 7106, pp. 213–222. Springer, Heidelberg (2011) 
-----2
1. Polanczyk, G., de Lima, M.S., Horta, B.L., Biederman, J., Rohde, L.A.: The world- wide prevalence of ADHD: a systematic review and metaregression analysis. Amer- ican Journal of Psychiatry 164(6), 942–948 (2007) 
2. Diagnostic and statistical manual of mental disorders, IV edition, Text Revision.American Psychiatric Association, Washington D. C (2000) 
3. Banaschewski, T., Becker, K., Scherag, S., Franke, B., Coghill, D.: Molecular genet- ics of attention-deficit/hyperactivity disorder: An overview. European Child and Adolescent Psychiatry 19(3), 237–257 (2010) 
4. Cortese, S.: The neurobiology and genetics of Attention-Deficit/Hyperactivity Dis- order (ADHD): What every clinician should know. European Journal of Paediatric Neurology 16(5), 422–433 (2012) 394 B.S. Mahanand, R. Savitha, and S. Suresh 
5. Cherkasova, M.V., Hechtman, L.: Neuroimaging in attention deficit hyperactiv- ity disorder: beyond the frontostriatal circuitry. Canadian Journal of Psychia- try 54(10), 651–664 (2009) 
6. Giedd, J.N., Rapoport, J.L.: Structural MRI of pediatric brain development: what have we learned and where are we going? Neuron. 67(5), 728–734 (2010) 
7. Ivanov, I., Bansal, R., Hao, X., Zhu, H., Kellendonk, C., Miller, L., Sanchez-Pena, J., Miller, A.M., Chakravarty, M.M., Klahr, K., Durkin, K., Greenhill, L.L., Pe- terson, B.S.: Morphological abnormalities of the thalamus in youths with atten- tion deficit hyperactivity disorder. Americal Journal of Psychiatry 167(4), 397–408 (2010) 
8. Shaw, P., Lerch, J., Greenstein, D., Sharp, W., Clasen, L., Evans, A., Giedd, J., Castellanos, F.X., Rapoport, J.: Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. Archives of General Psychiatry 63(5), 540–549 (2006) 
9. Jack, C.R., Petersen, R.C., O’Brien, P.C., Tangalos, E.G.: MR-based hippocampal volumetry in the diagnosis of Alzheimer’s disease. Neurology 42(1), 183–188 (1992) 
10. Milham, P.M., Damien, F., Maarten, M., Stewart, H.M.: The ADHD-200 consor- tium: A model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in Systems Neuroscience 6, 1–5 (2012) 
11. Maldjian, J.A., Laurienti, P.J., Kraft, R.A., Burdette, J.H.: An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage 19(3), 1233–1239 (2003) 
12. Babu, G.S., Suresh, S.: Meta-cognitive rbf network and its projection based learn- ing algorithm for classification problems. Applied Soft. Computing Journal 13(1), 654–666 (2013) 
13. Joysula, D.P., Vadali, H., Donahue, J., Hughes, F.C.: Modeling meta-cognition for learning in artificial systems. In: World Congress on Nature and Biologically Inspired Computing, pp. 1419–1424 (2009) 
14. Suresh, S., Dong, K., Kim, H.: A sequential learning algorithm for self-adaptive re- source allocation network classifier. Neurocomputing 73(16-18), 3012–3019 (2010) 
15. Mahanand, B.S., Suresh, S., Sundararajan, N., Kumar, M.A.: Identification of brain regions responsible for Alzheimer’s disease using a self-adaptive resource allocation network. Neural Networks 32, 313–322 (2012) 
16. Babu, G.S., Suresh, S.: Sequential projection-based metacognitive learning in a radial basis function network for classification problems. IEEE Transactions on Neural Networks and Learning Systems 24(2), 194–206 (2013) 
17. Babu, G.S., Suresh, S., Mahanand, B.S.: A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson’s disease. Expert Systems with Applications (2013), doi:10.1016/j.eswa.2013.07.073 
18. Subramanian, K., Suresh, S., Sundararajan, N.: A meta-cognitive neuro-fuzzy in- ference system (McFIS) for sequential classification problems. IEEE Transactions Fuzzy Systems (2013), doi:10.1109/TFUZZ.2013.2242894 
19. Nelson, T.O., Narens, L.: Metamemory: A theoretical framework and new findings.Psychology of Learning and Motivation 26, 125–173 (1990) 
20. Babu, G.S., Suresh, S.: Metacognitive neural network for classification problems in a sequential learning framework. Neurocomputing 81(1), 86–96 (2011) 
21. Suresh, S., Savitha, R., Sundararajan, N.: A sequential learning algorithm for com- plex valued self regulating resource allocation network-CSRAN. IEEE Transactions Neural Networks 22(7), 1061–1072 (2011) Computer Aided Diagnosis of ADHD Using Brain MR Images 395 
22. Savitha, R., Suresh, S., Sundararajan, N.: Metacognitive learning in a fully complex-valued radial basis function neural network. Neural Computation 24(5), 1297–1328 (2012) 
23. Savitha, R., Suresh, S., Sundararajan, N.: A meta-cognitive learning algorithm for a fully complex-valued relaxation network. Neural Networks 32, 209–218 (2012) 
24. Subramanian, K., Suresh, S.: A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system. Applied Soft. Computing 12(11), 3603–3614 (2012) 
25. Suresh, S., Subramanian, K.: A sequential learning algorithm for meta-cognitive neuro-fuzzy inference system for classification problems. In: Proceedings of Inter- national Joint Conference on Neural Networks, pp. 2507–2512 (2011) 
26. Ashburner, J.: A fast diffeomorphic image registration algorithm. NeuroIm- age 38(1), 95–113 (2007) 
27. Frodl, T., Skokauskas, N.: Meta-analysis of structural MRI studies in children and adults with attention deficit hyperactivity disorder indicates treatment effects.Acta Psychiatrica Scandinavica 125(2), 114–126 (2012) 
28. Bledsoe, J.C., Semrud-Clikeman, M., Pliszka, S.R.: Neuroanatomical and neu- ropsychological correlates of the cerebellum in children with attention-deficit/ hy- peractivity disorder-combined type. Journal of the American Academy of Child and Adolescent Psychiatry 50(6), 593–601 (2011) 
29. Colby, J.B., Rudie, J.D., Brown, J.A., Douglas, P.K., Cohen, M.S., Shehzad, Z.: Insights into multimodal imaging classification of ADHD. Frontiers in Systems Neuroscience 6(59), 1–18 (2012) 
-----2
1. Carlson, E.T., Rasquinha, R.J., Zhang, K., Connor, C.E.: A sparse object coding scheme in area v4. Current Biology 21(4), 288–293 (2011) 
2. Felleman, D.J., Van Essen, D.C.: Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex 1(1), 1–47 (1991) 
3. Friston, K.: Learning and inference in the brain. Neural Networks 16(9), 1325–1352 (2003) 
4. Fu, M., Yu, X., Lu, J., Zuo, Y.: Repetitive motor learning induces coordinated formation of clustered dendritic spines in vivo. Nature 483(7387), 92–95 (2012) Evaluating Sparse Codes on Handwritten Digits 407 
5. George, D., Hawkins, J.: A hierarchical Bayesian model of invariant pattern recog- nition in the visual cortex. In: Proceedings of the International Joint Conference on Neural Networksm, IJCNN 2005, pp. 1812–1817 (2005) 
6. Hawkins, J., Ahmad, S., Dubinsky, D.: Hierarchical temporal memory including HTM cortical learning algorithms. Tech. rep., Numenta, Inc, Palto Alto (2011), https://www.groksolutions.com/technology.html#cla-whitepaper 
7. Hawkins, J., Blakeslee, S.: On intelligence. Henry Holt, New York (2004) 
8. Hawkins, J., George, D.: Hierarchical temporal memory: Concepts, theory and ter- minology. Tech. rep., Numenta, Inc, Palto Alto (2006), http://www.numenta.com/ htm-overview/education/Numenta HTM Concepts.pdf 
9. Hyva¨rinen, A., Hurri, J., Hoyer, P.: Natural Image Statistics: A probabilistic ap- proach to early computational vision. Springer-Verlag, New York Inc. (2009) 
10. Hyva¨rinen, A., Karhunen, J., Oja, E.: Independent Components Analysis. John Wiley and Sons, Inc., New York (2001) 
11. Le, Q.V., Zou, W.Y., Yeung, S.Y., Ng, A.Y.: Learning hierarchical invariant spatio- temporal features for action recognition with independent subspace analysis. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3361–3368 (2011) 
12. LeCun, Y., Cortes, C.: MNIST handwritten digit database. AT&T Labs (1998), http://yann.lecun.com/exdb/mnist 
13. Lee, T.S., Mumford, D.: Hierarchical Bayesian inference in visual cortex. Journal of the Optical Society of America A 20(7), 1434–1448 (2003) 
14. Malone, B.J., Kumar, V.R., Ringach, D.L.: Dynamics of receptive field size in primary visual cortex. Journal of neurophysiology 97(1), 407–414 (2007) 
15. Mountcastle, V.B.: Introduction to the special issue on computation in cortical columns. Cerebral Cortex 13(1), 2–4 (2003) 
16. Olshausen, B.A., et al.: Emergence of simple-cell receptive field properties by learn- ing a sparse code for natural images. Nature 381(6583), 607–609 (1996) 
17. Stone, J.V.: Independent component analysis. Wiley Online Library (2004) 
18. Thornton, J., Main, L., Srbic, A.: Fixed frame temporal pooling. In: Thielscher, M., Zhang, D. (eds.) AI 2012. LNCS, vol. 7691, pp. 707–718. Springer, Heidelberg (2012) 
19. Thornton, J., Srbic, A.: Spatial pooling for greyscale images. International Journal of Machine Learning and Cybernetics 4, 207–216 (2013) 
20. Thornton, J., Srbic, A., Main, L., Chitsaz, M.: Augmented spatial pooling. In: Wang, D., Reynolds, M. (eds.) AI 2011. LNCS, vol. 7106, pp. 261–270. Springer, Heidelberg (2011) 
21. Willmore, B.D., Mazer, J.A., Gallant, J.L.: Sparse coding in striate and extrastriate visual cortex. Journal of Neurophysiology 105(6), 2907–2919 (2011) 
22. Willmore, B., Tolhurst, D.J.: Characterizing the sparseness of neural codes. Net- work: Computation in Neural Systems 12(3), 255–270 (2001) 
23. Willmore, B., Watters, P.A., Tolhurst, D.J.: A comparison of natural-image-based models of simple-cell coding. Perception-London 29(9), 1017–1040 (2000) 
-----2
1. Nelder, J.A., Wedderburn, R.W.M.: Generalized linear models. Journal of the Royal Statistical Society. Series A (General) 135(3), 370–384 (1972) 
2. Hoerl, A., Kennard, R.: Ridge regression. In: Encyclopedia of Statistical Sciences, vol. 8, pp. 129–136. Wiley, New York (1988) 
3. Wallace, C.S., Boulton, D.M.: An information measure for classification. Computer Journal 11(2), 185–194 (1968) 
4. Wallace, C.S., Freeman, P.R.: Estimation and inference by compact coding. Journal of the Royal Statistical Society (Series B) 49(3), 240–252 (1987) 
5. Wallace, C.S.: Statistical and Inductive Inference by Minimum Message Length.Information Science and Statistics. Springer (2005) 
6. Farr, G.E., Wallace, C.S.: The complexity of strict minimum message length infer- ence. Computer Journal 45(3), 285–292 (2002) 
7. Makalic, E., Schmidt, D.F.: Minimum message length shrinkage estimation. Statis- tics & Probability Letters 79(9), 1155–1161 (2009) 
8. Schmidt, D.F., Makalic, E.: MML invariant linear regression. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS, vol. 5866, pp. 312–321. Springer, Heidelberg (2009) 
9. Makalic, E., Schmidt, D.F.: MML logistic regression with translation and rotation invariant priors. In: Thielscher, M., Zhang, D. (eds.) AI 2012. LNCS, vol. 7691, pp.878–889. Springer, Heidelberg (2012) 
10. McCullagh, P., Nelder, J.A.: Generalized Linear Models, 2nd edn. Chapman & Hall/CRC (1989) 420 D.F. Schmidt and E. Makalic 
11. McQuarrie, A.D.R., Tsai, C.L.: Regression and Time Series Model Selection. World Scientific (1998) 
12. Kullback, S., Leibler, R.A.: On information and sufficiency. The Annals of Math- ematical Statistics 22(1), 79–86 (1951) 
13. Asuncion, A., Newman, D.: UCI machine learning repository (2007) 
14. Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. The Annals of Statistics 32(2), 407–451 (2004) 
15. Tibshirani, R.: Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society (Series B) 58(1), 267–288 (1996) 
-----2
1. Garcia, S., Derrac, J., Cano, J.R., Herrera, F.: Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(3), 417–435 (2012) 
2. http://sci2s.ugr.es/pr/ (April 18, 2013) 
3. Kim, S., Oommen, B.J.: On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 455–460 (2005) 
4. Triguero, I., Derrac, J., Garcia, S., Herrera, F.: A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification. IEEE Trans- actions on Systems, Man and Cybernetics - Part C: Applications and Reviews 42, 86–100 (2012) 
5. Duch, W.: Similarity Based Methods: A General Framework for Classification, Approximation and Association. Control and Cybernetics 29(4), 937–968 (2000) 
6. Foody, G.M.: Issues in Training Set Selection and Refinement for Classification by a Feedforward Neural Network. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, pp. 409–411 (1998) 
7. Foody, G.M.: The Significance of Border Training Patterns in Classification by a Feedforward Neural Network using Back Propogation Learning. International Journal of Remote Sensing 20(18), 3549–3562 (1999) 
8. Li, G., Japkowicz, N., Stocki, T.J., Ungar, R.K.: Full Border Identification for Reduction of Training Sets. In: Bergler, S. (ed.) Canadian AI. LNCS (LNAI), vol. 5032, pp. 203–215. Springer, Heidelberg (2008) 
9. Oommen, B.J., Thomas, A.: Optimal Order Statistics-based “Anti-Bayesian” Para- metric Pattern Classification for the Exponential Family. Pattern Recognition (2013) (accepted for Publication) 
10. Thomas, A., Oommen, B.J.: The Fundamental Theory of Optimal “Anti-Bayesian” Parametric Pattern Classification Using Order Statistics Criteria. Pattern Recog- nition 46, 376–388 (2013) 
11. Thomas, A., Oommen, B.J.: Order Statistics-based Parametric Classification for Multi-dimensional Distributions (submitted for publication 2013) 
12. Kim, S., Oommen, B.J.: A brief Taxonomy and Ranking of Creative Prototype Reduction Schemes. Pattern Analysis and Applications 6, 232–244 (2003) 
13. Devroye, L.: Non-Uniform Random Variate Generation. Springer, New York (1986) 
14. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, San Diego (1990) 
15. Frank, A., Asuncion, A.: UCI Machine Learning Repository (2010), http://archive.ics.uci.edu/ml (April 18, 2013) Order Statistics-Based “Anti-Bayesian” PRSs 433 
16. http://www.is.umk.pl/projects/datasets.html (April 18, 2013) 
17. Karegowda, A.G., Jayaram, M.A., Manjunath, A.S.: Cascading K-means Cluster- ing and k-Nearest Neighbor Classifier for Categorization of Diabetic Patients. In- ternational Journal of Engineering and Advanced Technonlogy 01, 147–151 (2012) 
18. Salama, G.I., Abdelhalim, M.B., Elghany Zeid, M.A.: Breast Cancer Diagnosis on Three Different Datasets using Multi-classifiers. International Journal of Computer and Information Technology 01, 36–43 (2012) 
-----2
1. Baskin, J.P., Krishnamurthi, S.: Preference aggregation in group recommender sys- tems for committee decision-making. In: RecSys, pp. 337–340 (2009) 
2. Berkovsky, S., Freyne, J.: Group-based recipe recommendations: analysis of data aggregation strategies. In: RecSys, pp. 111–118 (2010) 
3. Parameswaran, A.G., Koutrika, G., Bercovitz, B., Molina, H.G.: Recsplorer: recom- mendation algorithms based on precedence mining. In: SIGMOD, pp. 87–98 (2010) 440 V.R. Kagita, A.K. Pujari, and V. Padmanabhan 
4. Michael, J.: Pazzani. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13, 393–408 (1999) 
5. De Pessemier, T., Dooms, S., Martens, L.: Design and evaluation of a group recom- mender system. In: RecSys, pp. 225–228 (2012) 
6. Seko, S., Yagi, T., Motegi, M., Muto, S.Y.: Group recommendation using feature space representing behavioral tendency and power balance among members. In: RecSys, pp. 101–108 (2011) 
-----2
1. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, 1–30 (2006) 
2. Mitchell, T.: Machine Learning. McGraw Hill, Ohio (1997) 
3. Naseem, I., Togneri, R., Bennamoun, M.: Linear regression for face recognition.PAMI 32(11), 2106–2112 (2010) 
4. Scholkopf, B., Smola, A., Williamson, B., Bartlett, P.: New support vector algorithm.Neural Computation 12, 1207–1245 (2000) 
5. Tax, D., Duin, R.: Support vector domain description. Pattern Recognition Let- ters 20, 1191–1199 (1999) 
6. Thomas, A., Oommen, B.J.: The fundamental theory of optimal “anti-Bayesian” parametric pattern classification using order statistics criteria. Pattern Recogni- tion 46, 376–388 (2013) 
7. Tibshirani, R., Hastie, T., Narasimhan, B., Chu, G.: Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Statistical Science 18(1), 104–117 (2003) 
8. Vapnik, V.: Statistical Learning Theory. Wiley-IEEE Press, New York (1998) 
-----1
[1] Chickering, D.M.: Learning Bayesian networks is NP-complete. In: Learning from data, pp. 121–130. Springer (1996) 
[2] Chow, C., Liu, C.: Approximating discrete probability distributions with depen- dence trees. IEEE Transactions on Information Theory 14(3), 462–467 (1968) 
[3] Dasgupta, S.: Learning polytrees. In: Uncertainty in Artificial Intelligence, pp. 134–141 (1999) 
[4] Edmonds, J.: Optimum branchings. Journal of Research of the National Bureau of Standards B 71, 233–240 (1967) 
[5] Gaspers, S., Koivisto, M., Liedloff, M., Ordyniak, S., Szeider, S.: On finding optimal polytrees. In: Twenty-Sixth AAAI Conference on Artificial Intelligence (2012) 
[6] Tarjan, R.E.: Finding optimum branchings. Networks 7(1), 25–35 (1977) 
[7] Verma, T.S., Pearl, J.: Equivalence and synthesis of causal models. In: Uncertainty in Artificial Intelligence (UAI). pp. 220–227 (1990) 
[8] Wilf, H.S.: Generatingfunctionology. Academic Press (1990) 
-----2
1. Wang, X., Fang, H., Zhai, C.: A study of methods for negative relevance feed- back. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 219–226. ACM (2008) 464 M. Albathan, Y. Li, and A. Algarni 
2. Wang, X., McCallum, A., Wei, X.: Topical n-grams: Phrase and topic discovery, with an application to information retrieval. In: Seventh IEEE International Con- ference on Data Mining, ICDM 2007, pp. 697–702. IEEE (2007) 
3. Liu, H., Motoda, H., Setiono, R., Zhao, Z.: Feature selection: An ever evolving frontier in data mining. In: Proc. The Fourth Workshop on Feature Selection in Data Mining, vol. 4, pp. 4–13 (2010) 
4. Li, Y., Zhong, N.: Mining ontology for automatically acquiring web user informa- tion needs. IEEE Transactions on Knowledge and Data Engineering 18(4), 554–568 (2006) 
5. Berry, M.W., Kogan, J.: Text mining: applications and theory. Wiley (2010) 
6. Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28(1), 11–21 (1972) 
7. Hulth, A.: Improved automatic keyword extraction given more linguistic knowl- edge. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Lan- guage Processing, pp. 216–223. Association for Computational Linguistics (2003) 
8. Tandon, N., de Melo, G.: Information extraction from web-scale n-gram data. In: Web N-gram Workshop, vol. 7, Citeseer (2010) 
9. Wei, Z., Chauchat, J., Miao, D.: Comparing different text representation and feature selection methods on chinese text classification using character n-grams.Journe´es Internationnales d’Analyse des Donne´es Textuelles, 1175–1186 (2008) 
10. Manning, C.D., Raghavan, P., Schu¨tze, H.: Introduction to information retrieval, vol. 1. Cambridge University Press, Cambridge (2008) 
11. Hiemstra, D.: Using language models for information retrieval. Taaluitgeverij Neslia Paniculata (2001) 
12. Wang, K., Thrasher, C., Viegas, E., Li, X., Hsu, B.j.P.: An overview of mi- crosoft web n-gram corpus and applications. In: Proceedings of the NAACL HLT 2010 Demonstration Session, pp. 45–48. Association for Computational Linguistics (2010) 
13. Li, Y., Algarni, A., Zhong, N.: Mining positive and negative patterns for relevance feature discovery. In: Proceedings of the 16th ACM SIGKDD International Con- ference on Knowledge Discovery and Data Mining, KDD 2010, pp. 753–762. ACM, New York (2010) 
14. Wu, S.T.: Knowledge discovery using pattern taxonomy model in text mining. PhD thesis, Queensland University of Technology (2007) 
15. Liu, B.: Web data mining: exploring hyperlinks, contents, and usage data. Springer (2007) 
16. Wei, Z., Miao, D., Chauchat, J.H., Zhao, R., Li, W.: N-grams based feature selec- tion and text representation for chinese text classification. International Journal of Computational Intelligence Systems 2(4), 365–374 (2009) 
17. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003) 
18. Fu¨rnkranz, J.: A study using n-gram features for text categorization. Austrian Research Institute for Artifical Intelligence 3(1998), 1–10 (1998) 
19. Bertolami, R., Bunke, H.: Integration of n-gram language models in multiple clas- sifier systems for offline handwritten text line recognition. International Journal of Pattern Recognition and Artificial Intelligence 22(07), 1301–1321 (2008) 
20. Li, Y.: Extended random sets for knowledge discovery in information systems. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 524–532. Springer, Heidelberg (2003) Enhanced N-Gram Extraction Using Relevance Feature Discovery 465 
21. Joachims, T.: A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. Technical report, DTIC Document (1996) 
22. Robertson, S., Soboroff, I.: The trec 2002 filtering track report. In: Text REtrieval Conference (2002) 
23. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval.Information processing & management 24(5), 513–523 (1988) 
-----2
1. Agirre, E., Bengoetxea, K., Gojenola, K., Nivre, J.: Improving dependency parsing   with semantic classes. In: Proceedings of the 49th Annual Meeting of the Association   for Computational Linguistics: Human Language Technologies, ACL HLT 2011, Portland,  pp. 699–703 (2011)  
2. Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. Journal of Machine Learning Re- search 3, 993–1022 (2003)   Generating Context Templates for Word Sense Disambiguation 477  
3. Cai, J.F., Lee, W.S., Teh, Y.W.: Improving word sense disambiguation using topic fea- tures. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Lan- guages Processing and Computational Natural Language Learning, pp. 1015–1023 (2007)  
4. Dagan, I., Lee, L., Pereira, F.: Similarity-based models of word co-occurrence probabili- ties. Machine Learning Journal 3, 1–3, 43–69 (1999)  
5. Decadt, B., Hoste, V., Daelemans, W., van den Bosch, A.: GAMBL, Genetic Algorithm  Optimization of Memory-Based WSD. In: SENSEVAL-3: Third International Workshop  on the Evaluation of Systems for the Semantic Analysis of Text (2004)  
6. Di Sciullo, A.M., Williams, E.: On the Definition of Word. In: Linguistic Inquiry Mono- graph, vol. 14, MIT Press, Cambridge (1987)  
7. Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)  
8. Freeman, L.C.: Centrality in social networks conceptual clarification. Social Networks 1,  215–239 (1977)  
9. Jiang, J.J., Conrath, D.W.: Semantic Similarity Based on Corpus Statistics and Lexical  Taxonomy. In: Proceedings of International Conference on Research in Computational  Linguistics, pp. 19–33. International Committee on Computational Linguistics (1997)  
10. Ker, S.-j., Huang, C.-R., Hong, J.-F., Liu, S.-Y., Jian, H.-L., Su, I.-L., Hsieh, S.-K.: Design  and Prototype of a Large-scale and Fully Sense-tagged Corpus. In: Tokunaga, T., Ortega,  A. (eds.) LKP 2008. LNCS (LNAI), vol. 4938, pp. 186–193. Springer, Heidelberg (2008)  
11. Mackinlay, A., Dridan, R., Mccarthy, D., Baldwin, T.: The effects of semantic annotations  on precision parse ranking. In: Proceedings of the First Joint Conference on Lexical and  Computational Semantics (*SEM 2012), Montreal, pp. 228–236 (2012)  
12. Mei, J., Zhu, Y., Gao, Y., Ying, H.: Tongyici Cilin. Commercial Press (1984) (in Chinese)  
13. Mihalcea, R., Csomai, A.: SenseLearner: word sense disambiguation for all words in unre- stricted text. In: Proceedings of the ACL 2005 on Interactive Poster and Demonstration  Sessions, pp. 53–56 (2005)  
14. Navigli, R., Lapata, M.: Graph connectivity measures for unsupervised word sense disam- biguation. In: Proceedings of IJCAI, pp. 1683–1688 (2007)  
15. Newman, M.: Networks: An Introduction. Oxford (2011)  
16. Steyvers, M., Griffiths, T.: Probabilistic Topic Models. In: Landauer, T., Mcnamara, D.,  Dennis, S., Kintsch, W. (eds.) Handbook of Latent Semantic Analysis (2007)  
17. Tsatsaronis, G., Varlamis, I., Nørvåg, K.: An experimental study on unsupervised graph- based word sense disambiguation. In: Gelbukh, A. (ed.) CICLing 2010. LNCS, vol. 6008,  pp. 184–198. Springer, Heidelberg (2010)  
18. Wu, Y., Jin, P., Guo, T., Yu, S.: Building Chinese sense annotated corpus with the help of  software tools. In: Proceedings of the Linguistic Annotation Workshop. ACL, Prague  (2007)  
-----2
1. Burke, E., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R.: Hyper-heuristics: A survey of the state of the art. Computer Science Technical Report No. NOTTCS- TR-SUB-0906241418-2747 (2010) 
2. Cesaret, B., Og?uz, C., Salman, F.S.: A tabu search algorithm for order acceptance and scheduling. Computers & Operations Research 39(6), 1197–1205 (2012) 
3. Dimopoulos, C., Zalzala, A.: Investigating the use of genetic programming for a classic one-machine scheduling problem. Advances in Engineering Software 32(6), 489–498 (2001) 
4. Geiger, C.D., Uzsoy, R.: Learning effective dispatching rules for batch processor scheduling. International Journal of Production Research 46(6), 1431–1454 (2008) 
5. Geiger, C.D., Uzsoy, R., Aytug, H.: Rapid modeling and discovery of priority dis- patching rules: An autonomous learning approach. Journal of Scheduling 9(1), 7–34 (2006) 
6. Ghosh, J.B.: Job selection in a heavily loaded shop. Computers & Operations Research 24(2), 141–145 (1997) 
7. Hildebrandt, T., Heger, J., Scholz-Reiter, B.: Towards improved dispatching rules for complex shop floor scenarios: A genetic programming approach. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp.257–264 (2010) 
8. Jakobovic´, D., Budin, L.: Dynamic scheduling with genetic programming. In: Col- let, P., Tomassini, M., Ebner, M., Gustafson, S., Eka´rt, A. (eds.) EuroGP 2006.LNCS, vol. 3905, pp. 73–84. Springer, Heidelberg (2006) 
9. Luke, S.: Essentials of Metaheuristics. Lulu (2009) 
10. Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Learning reusable initial solu- tions for multi-objective order acceptance and scheduling problems with genetic programming. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.S¸., Hu, B.(eds.) EuroGP 2013. LNCS, vol. 7831, pp. 157–168. Springer, Heidelberg (2013) 
11. Og?uz, C., Salman, F.S., Yalin, Z.B.: Order acceptance and scheduling decisions in make-to-order systems. International Journal of Production Economics 125(1), 200–211 (2010) 
12. Park, J., Nguyen, S., Zhang, M., Johnston, M.: Genetic programming for order acceptance and scheduling. In: IEEE Congress on Evolutionary Computation, pp.1005–1012 (to appear, 2013) 
13. Rom, W.O., Slotnick, S.A.: Order acceptance using genetic algorithms. Computers & Operations Research 36(6), 1758–1767 (2009) 
14. Roundy, R., Chen, D., Chen, P., Cakanyildirim, M., Freimer, M.B., Melkonian, V.: Capacity-driven acceptance of customer orders for a multi-stage batch manufac- turing system: models and algorithms. IIE Transactions 37(12), 1093–1105 (2005) 
15. Slotnick, S.A.: Order acceptance and scheduling: A taxonomy and review. Euro- pean Journal of Operational Research 212(1), 1–11 (2011) 
16. Slotnick, S.A., Morton, T.E.: Order acceptance with weighted tardiness. Computers & Operations Research 34(10), 3029–3042 (2007) 
17. Wester, F.A.W., Wijngaard, J., Zijm, W.R.M.: Order acceptance strategies in a production-to-order environment with setup times and due-dates. International Journal of Production Research 30(6), 1313–1326 (1992) 
-----2
1. Berend, D., Kontorovich, A.: The missing mass problem. Stat. and Prob. Lett. 82, 1102–1110 (2012) 
2. Blum, A.L., Furst, M.L.: Fast planning through planning graph analysis. Artif.Intell. 90(1), 1636–1642 (1995) 
3. Bonet, B., Geffner, H.: Planning as heuristic search. Artif. Intell. 129(1-2), 5–33 (2001) 
4. Chen, Y., Xing, Z., Zhang, W.: Long-distance mutual exclusion for propositional planning. In: IJCAI, pp. 1840–1845 (2007) 
5. Culberson, J., Schaeffer, J.: Pattern databases. Comput. Intell. 14(3), 318–334 (1998) 
6. Dawson, C., Siklo´ssy, L.: The role of preprocessing in problem solving systems. In: IJCAI, pp. 465–471 (1977) 
7. Haslum, P.: Admissible Heuristics for Automated Planning. Linko¨ping Studies in Science and Technology: Dissertations. Dept. of Computer and Information Sci- ence. Linko¨pings Univ. (2006) 
8. Haslum, P., Bonet, B., Geffner, H.: New admissible heuristics for domain- independent planning. In: AAAI, pp. 1163–1168 (2005) 
9. Helmert, M.: The Fast Downward planning system. J. Artif. Intell. Res. 26, 191–246 (2006) 
10. Helmert, M., Lasinger, H.: The Scanalyzer domain: Greenhouse logistics as a plan- ning problem. In: ICAPS, pp. 234–237 (2010) 
11. Herna´dvo¨lgyi, I., Holte, R.: PSVN: A vector representation for production systems.Technical Report TR-99-04, Dept. of Computer Science, Univ. of Ottawa (1999) 
12. Ribeiro, B.F., Towsley, D.F.: Estimating and sampling graphs with multidimen- sional random walks. CoRR abs/1002.1751 (2010) 
13. Sadeqi, M., Holte, R.C., Zilles, S.: Using coarse state space abstractions to detect mutex pairs. In: SARA, pp. 104–111 (2013) 
14. Thayer, J., Ruml, W.: Bounded suboptimal search: A direct approach using inad- missible estimates. In: IJCAI 2011, pp. 674–679 (2011) 
15. Zilles, S., Holte, R.C.: The computational complexity of avoiding spurious states in state space abstraction. Artif. Intell. 174, 1072–1092 (2010) 
-----2
1. Mukherjee, S., Davulcu, H., Kifer, M., Senkul, P., Yang, G.: Logic-based approaches to workflow modeling and verification. In: Chomicki, J., Meyden, R., Saake, G.(eds.) Logics for Emerging Applications of Databases, pp. 167–202. Springer, Heidelberg (2004) 
2. Oren, E., Haller, A.: Formal frameworks for workflow modelling. Digital Enterprise Research Institute, National University of Ireland, Technical Report, vol. 20, pp.04–07 (2005) 
3. Drozdowski, M.: Scheduling for Parallel Processing. Springer Publishing Company, Incorporated (2009) 
4. Graham, R.L., Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.: Optimization and approximation in deterministic sequencing and scheduling: A survey. Annals of Discrete Mathematics 5, 287–326 (1979) 
5. Allahverdi, A., Gupta, J.N., Aldowaisan, T.: A review of scheduling research in- volving setup considerations. Omega 27(2), 219–239 (1999) 
6. Kwok, Y.-K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Computing Surveys (CSUR) 31(4), 406–471 (1999) 
7. Ahmad, I., Kwok, Y.-K.: On exploiting task duplication in parallel program scheduling. IEEE Transactions on Parallel and Distributed Systems 9(9), 872–892 (1998) 
8. Kwok, Y.-K., Ahmad, I.: Exploiting duplication to minimize the execution times of parallel programs on message-passing systems. In: Proceedings of the Sixth IEEE Symposium on Parallel and Distributed Processing, pp. 426–433. IEEE (1994) 
9. Topcuoglu, H., Hariri, S., Wu, M.-Y.: Task scheduling algorithms for heterogeneous processors. In: Proceedings of the Eighth Heterogeneous Computing Workshop, pp.3–14 (1999) 
10. Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems 13(3), 260–274 (2002) 
11. Ilavarasan, E., Thambidurai, P.: Low complexity performance effective task scheduling algorithm for heterogeneous computing environments. Journal of Com- puter Sciences 3(2), 94–103 (2007) 
12. Kim, S.C., Lee, S., Hahm, J.: Push-Pull: Deterministic search-based DAG schedul- ing for heterogeneous cluster systems. IEEE Transactions on Parallel and Dis- tributed Systems 18(11), 1489–1502 (2007) 
13. Tang, X., Li, K., Liao, G., Fang, K., Wu, F.: A stochastic scheduling algorithm for precedence constrained tasks on grid. Future Generation Computer Systems 27(8), 1083–1091 (2011) Scheduling for Optimal Response Times in Queues of Stochastic Workflows 513 
14. Coffman Jr., E., Flatto, L., Garey, M., Weber, R.: Minimizing expected makespans on uniform processor systems. In: Advances in Applied Probability, pp. 177–201 (1987) 
15. Rothkopf, M.H.: Scheduling with random service times. Management Science 12(9), 707–713 (1966) 
16. Pinedo, M.: Offline deterministic scheduling, stochastic scheduling, and online de- terministic scheduling: A comparative overview. In: Handbook of Scheduling — Algorithms, Models, and Performance Analysis (2004) 
17. Mo¨hring, R.H., Schulz, A.S., Uetz, M.: Approximation in stochastic scheduling: The power of LP-based priority policies. Fachbereich Mathematik, TU Berlin, Tech.Rep. 595-1998 (1998) 
18. Skutella, M., Uetz, M.: Stochastic machine scheduling with precedence constraints.SIAM Journal on Computing 34(4), 788–802 (2005) 
19. Chandy, K.M., Reynolds, P.F.: Scheduling partially ordered tasks with probabilistic execution times. ACM SIGOPS Operating Systems Review 9(5), 169–177 (1975) 
20. Iverson, M., Ozguner, F.: Dynamic, competitive scheduling of multiple DAGs in a distributed heterogeneous environment. In: Proceedings of the Seventh Heteroge- neous Computing Workshop, pp. 70–78. IEEE (1998) 
21. Sih, G.C., Lee, E.A.: A compile-time scheduling heuristic for interconnection- constrained heterogeneous processor architectures. IEEE Transactions on Parallel and Distributed Systems 4(2), 175–187 (1993) 
22. Bender, M.A., Rabin, M.O.: Online scheduling of parallel programs on hetero- geneous systems with applications to cilk. Theory of Computing Systems 35(3), 289–304 (2002) 
23. Zhao, H., Sakellariou, R.: Scheduling multiple DAGs onto heterogeneous systems.In: Proceedings of the 20th International Parallel and Distributed Processing Sym- posium, pp. 14–27. IEEE (2006) 
24. Wieczorek, M., Siddiqui, M., Villazon, A., Prodan, R., Fahringer, T.: Applying advance reservation to increase predictability of workflow execution on the grid.In: Proceedings of the Second IEEE International Conference on e-Science and Grid Computing, pp. 82–89. IEEE (2006) 
25. Calheiros, R.N., Ranjan, R., Buyya, R.: Virtual machine provisioning based on analytical performance and QoS in cloud computing environments. In: Proceedings of the International Conference on Parallel Processing, pp. 295–304. IEEE (2011) 
26. Muthuvelu, N., Vecchiola, C., Chai, I., Chikkannan, E., Buyya, R.: Task granularity policies for deploying bag-of-task applications on global grids. Future Generation Computer Systems 29(1), 170–181 (2013) 
27. Gallet, M., Marchal, L., Vivien, F.: Efficient scheduling of task graph collections on heterogeneous resources. In: Proceedings of the IEEE International Symposium on Parallel & Distributed Processing, pp. 1–11. IEEE (2009) 
28. Kendall, D.G.: Stochastic processes occurring in the theory of queues and their analysis by the method of the imbedded markov chain. The Annals of Mathematical Statistics 24, 338–354 (1953) 