A k-elitist max-min ant system approach to cost-based abduction

2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings

Volumn 4, Issue , 2003, Pages 2635-2641

  Abdelbar, A M ^a   Mokhtar, M ^a  

^a The American University in Cairo   (Egypt)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS;

MAX MIN ANT SYSTEM(MMAS); MAX-MIN ANT SYSTEM; STATISTICAL SIGNIFICANCE;

COSTS;

EID: 4644272591     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2003.1299420     Document Type: Conference Paper

References (21)

1. A. M. Abdelbar. An algorithm for finding MAPs for belief networks through cost-based abduction. Artificial Intelligence. Vol. 104. pp. 331-338. 1998.
2. A. M. Abdelbar, and H. Amer. Applying guided evolutionary simulated annealing to cost-based abduction. In Proceedings IEEE International Joint Conference on Neural Networks. 2003.
3. A. M. Abdelbar, E. A. M. Andrews, and D. C. Wunsch. Abductive reasoning with recurrent neural networks. Neural Networks, 2003, to appear.
4. B. Bullnheimer, R. F. Hartl, and C. Strauss. An improved ant system algorithm for the vehicle routing problem. Annals of Operations Research, Vol. 89, 1999.
5. E. Charniak. and S. E. Shimony. Probabilistic semantics for cost-based abduction. in: Proceedings AAAI National Conference on Artificial Intelligence. 1990.
6. E. Charniak, and S. Shimony. Cost-based abduction and MAP explanation. Artificial Intelligence, Vol. 66, pp. 345-374, 1994.
7. Y. Den. Generalized chart algoritm: an efficient procedure for cost-based abduction. In Proceedings Annual Meeting of the Association for Computational Linguistics. 1994.
8. M. Dorigo. Optimization, Learning, and Natural Algorithms. Ph. D. dissertation, DEI, Politecnico di Milano. Italy, 1992 (in Italian).
9. M. Dorigo, and L. M. Gambardella. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, Vol. 1, pp. 53-66, 1997.
10. M. Dorigo, V. Maniezzo. and A. Colorni. Ant system: optimization by a colony of cooperative agents. IEEE Transactions on Systems, Man. and Cybernetics, Vol. 26. pp. 29-41, 1996.
11. W. C. Guenther. Analysis of Variance. Prentice-Hall, Englewood Cliffs, 1964.
12. J. R. Hobbs, M. E. Stickel, P. Martin. & D. Edwards. Interpretation as abduction. Artificial Intelligence, 63. pp. 69-130, 1993.
13. V. Maniezzo, and A. Colomi. The ant system applied to the quadratic assignment problem. IEEE Transactions on Knowledge and Data Engineering. Vol. 11, pp. 769-778, 1999.
14. G. L. Nemhauser, A. H. G. Rinnooy Kan, and M. J. Todd. Optimization: Handbooks in Operations Research and Management Science, Vol. 1, North Holland. Amsterdam. 1989.
15. Y. Ohsawa, and M. Ishizuka. Networked bubble propagation: a polynomial-time hypothetical reasoning method for computing near-optimal solutions. Artificial Intelligence. Vol. 91, pp. 131-154, 1997.
16. E. Santos Jr. On the generation of alternative explanations with implications for belief revision. In Proceedings Seventh Conference on Uncertainty in AI. 1991.
17. E. Santos Jr. A linear constraint satisfaction approach to cost-based abduction. Artificial Intelligence, 65 (1994) 1-27.
18. E. Santos Jr., and E. S. Santos. Polynomial solvability of cost-based abduction. Artificial Intelligence 86 (1996) 157-170.
19. A. Schriyver, Theory of Linear and Integer Programming, John Wiley & Sons, New York. 1986.
20. T. Stutzle. and H. H. Hoos. MAX-MIN Ant System. Future Generation Computer Systems, Vol. 16, pp. 889-914, 2000.
21. J. J. A. Tacq. Multivariate Analysis Techniques in Social Science Research, Sage Press. London, 1997.