Multi-agent, multi-case-based reasoning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7969 LNAI, Issue , 2013, Pages 74-88

  Epstein, Susan L ^a   Yun, Xi ^a   Xie, Lei ^a  

^a City University of New York   (United States)

Author keywords

confidence based reasoning; multiple agents; multiple cases

Indexed keywords

BIOLOGICAL DATA; CONFIDENCE-BASED REASONING; CONSTRAINT SATISFACTION; DRUG DISCOVERY; KNOWLEDGE BASE; MULTIPLE AGENTS; MULTIPLE CASES; PARALLEL PROCESSING;

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE;

KNOWLEDGE BASED SYSTEMS;

EID: 84884504655     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-39056-2_6     Document Type: Conference Paper

References (26)

1. Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1-15. Springer, Heidelberg (2000)
2. Leake, D.B., Sooriamurthi, R.: Automatically Selecting Strategies for Multi-Case-Base Reasoning. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 204-233. Springer, Heidelberg (2002)
3. Plaza, E., McGinty, L.: Distributed Case-Based Reasoning. The Knowledge Engineering Review 20(3), 261-265 (2005)
4. Redmond, M.: Distributed Cases for Case-Based Reasoning: Facilitating Use of Multiple Cases. In: Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI 1990), pp. 304-309 (1990)
5. Kar, D., Chakraborti, S., Ravindran, B.: Feature Weighting and Confidence Based Prediction for Case Based Reasoning Systems. In: Agudo, B.D., Watson, I. (eds.) ICCBR 2012. LNCS, vol. 7466, pp. 211-225. Springer, Heidelberg (2012)
6. Yun, X., Epstein, S.L.: Learning Algorithm Portfolios for Parallel Execution. In: Hamadi, Y., Schoenauer, M. (eds.) LION 2012. LNCS, vol. 7219, pp. 323-338. Springer, Heidelberg (2012)
7. Guerri, A., Milano, M.: Learning Techniques for Automatic Algorithm Portfolio Selection. In: Proceedings of the Sixteenth European Conference on Artificial Intelligence, pp. 475-479 (2004)
8. O'Mahony, E., Hebrard, E., Holland, A., Nugent, C., O'Sullivan, B.: Using Case-Based Reasoning in an Algorithm Portfolio for Constraint Solving. In: Proceedings of the Nineteenth Irish Conference on Artificial Intelligence and Cognitive Science (2008)
9. Silverthorn, B., Miikkulainen, R.: Latent Class Models for Algorithm Portfolio Methods. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, pp. 167-172 (2010)
10. Xu, L., Hoos, H.H., Leyton-Brown, K.: Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, pp. 210-216 (2010)
11. Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla: Portfolio-Based Algorithm Selection for SAT. Journal of Artificial Intelligence Research 32, 565-606 (2008)
12. Horvitz, E., Ruan, Y., Gomes, C.P., Kautz, H.A., Selman, B., Chickering, D.M.: A Bayesian Approach to Tackling Hard Computational Problems. In: Proceedings of the Seventeenth Conference in Uncertainty in Artificial Intelligence, pp. 235-244. Morgan Kaufmann Publishers Inc. (2001)
13. Streeter, M., Golovin, D., Smith, S.F.: Combing Multiple Heuristics Online. In: Proceedings of the Twenty-Second National Conference on Artificial Intelligence, pp. 1197-1203 (2007)
14. Mistral, http://4c.ucc.ie/~ehebrard/Software.html
15. Third International CSP Solver Competition (CPAI 2008), http://www.cril.univ-artois.fr/CPAI08/
16. Fourth International CSP Solver Competition (CSC 2009), http://www.cril.univ-artois.fr/CSC09/
17. Huang, S.-Y., Zou, X.: Advances and Challenges in Protein-Ligand Docking. International Journal of Molecular Science 11, 3016-3034 (2010)
18. Charifson, P.S., Corkery, J.J., Murcko, M.A., Walters, W.P.: Consensus Scoring: A Method for Obtaining Improved Hit Rates from Docking Databases of Three-Dimensional Structures into Proteins. Journal of Medicinal Chemistry 42, 5100-5109 (1999)
19. Clark, R.D., Strizhev, A., Leonard, J.M., Blake, J.F., Matthew, J.B.: Consensus Scoring for Ligand/Protein Interactions. Journal of Molecular Graphics Modelling 20, 281-295 (2002)
20. Wang, R., Wang, S.: How Does Consensus Scoring Work for Virtual Library Screening? An Idealized Computer Experiment. Journal of Chemical Information and Computer Sciences 41, 1422-1426 (2001)
21. Zsoldos, Z., Reid, D., Simon, A., Sadjad, B.S., Johnson, P.A.: Ehits: An Innovative Approach to the Docking and Scoring Function Problems. Current Protein and Peptide Science 7, 421-435 (2006)
22. Trott, O., Olson, A.J.: Autodock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization and Multithreading. Journal of Computational Chemistry 31, 455-461 (2010)
23. Morris, G.M., Goodsell, D.S., Halliday, R.S., Huey, R., Hart, W.E., Belew, R.K., Olson, A.J.: Automated Docking Using a Lamarckian Genetic Algorithm and Empirical Binding Free Energy Function. Journal of Computational Chemistry 19, 1639-1662 (1998)
24. Miteva, M.A., Lee, W.H., Montes, M.O., Villoutreix, B.O.: Fast Structure-Based Virtual Ligand Screening Combining Fred, Dock, and Surflex. Journal of Medicinal Chemistry 48, 6012-6022 (2005)
25. Fukunishi, H., Teramoto, R., Takada, T., Shimada, J.: Bootstrap-Based Consensus Scoring Method for Protein-Ligand Docking. Journal of Chemical Information and Modeling 48, 988-996 (2008)
26. Huang, N., Shoichet, B.K., Irwin, J.J.: Benchmarking Sets for Molecular Docking. Journal of Medicinal Chemistry 49, 6789-6801 (2006)