Performing Bayesian inference by weighted model counting

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2005, Pages 475-481

  Sang, Tian ^a   Beame, Paul ^a   Kautz, Henry ^a  

^a University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN INFERENCE; COMPONENT CACHING; WEIGHTED MODEL COUNTING;

ALGORITHMS; COMPUTATION THEORY; CONSTRAINT THEORY; PROBLEM SOLVING;

ARTIFICIAL INTELLIGENCE;

EID: 29344449175     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. D. Allen and A. Darwiche. New advances in inference by recursive conditioning. In Proceedings of the 19th Conference on Uncertainly in Arti cial Intelligence UAI-2003, pages 2-10, 2003.
2. F. Bacchus, S. Dalmao, and T. Pitassi. Algorithms and complexity results for #SAT and Bayesian inference. In Proceedings 44th IEEE FOCS 2003, pages 340-351, 2003.
3. F. Bacchus, S. Dalmao, and T. Pitassi. Value elimination: Bayesian inference via backtracking search. In Uncertainty in Arti cial Intelligence UAI-2003, pages 20-28, 2003.
4. R. J. Bayardo Jr. and R. C. Schrag. Using CSP look-back techniques to solve real-world SAT instances. In Proceedings, AAAI-97: 14th National Conference on Arti cial Intelligence, pages 203-208, 1997.
5. P. Beame, R. Impagliazzo, T. Pitassi, and N. Segerlind. Memoization and DPLL: Formula caching proof systems. In Proceedings 18th Annual IEEE Conference on Computational Complexity, pages 225-236, Aarhus. Denmark, July 2003.
6. J. Bilmes and G. Zweig. The graphical models toolkit: An open source software system for speech and time-series processing. In Proceedings of IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, 2002.
7. M. Chavira, A. Darwiche, and M. Jaeger. Compiling relational bayesian networks for exact inference. In Proceedings of the Second European Workshop on Probabilistic Graphical Models (PGM-2004), pages 49-56, 2004.
8. S. Cook and D. Mitchell. Finding hard instances of the satisfiability problem: A survey. In DIMACS Series in Theoretical Computer Science. 1997.
9. A. Darwiche. Recursive conditioning. Arti cial Intelligence, 125(1-2):5-41, 2001.
10. A. Darwiche. A logical approach to factoring belief networks. In Proceedings of International Conference on Knowledge Representation and Reasoning, pages 409-420, 2002.
11. M. Davis, G. Logemann, and D. Loveland. A machine program for theorem proving. Communications of the ACM, 5:394-397, 1962.
12. R. Dechter. Enhancement schemes for constraint processing: Backjumping, learning and cutset decomposition. Arti cial Intelligence, 41:273-312, 1990.
13. R. Dechter. Bucket elimination: A unifying framework for reasoning. Arti cial Intelligence, 113:41-85, 1999.
14. E. Goldberg and Y. Novikov. Berkmin: a fast and robust satsolver. In Proceedings of the Design and Test in Europe Conference, pages 142-149, March 2002.
15. H. Kautz and B. Selman. Unifying sat-based and graph-based planning. In Proceedings of the 16th International Joint Conference on Arti cial Intelligence (IJCAI-99), pages 318-325. Morgan Kaufmann, 1999.
16. H. Kautz and B. Selman. Ten challenges redux: Recent progress in prepositional reasoning and search. In Ninth International Conference on Principles and Practice of Constraint Programming CP 2003, 2003.
17. M. L. Littman. Initial experiments in stochastic satisfiability. In Proceedings of the Sixteenth National Conference on Arti cial Intelligence, pages 667-672, 1999.
18. S. M. Majercik and M. L. Littman. Using caching to solve larger probabilistic planning problems. In Proceedings of the 15th AAAI, pages 954-959, 1998.
19. J. P. Marques-Silva and K. A. Sakallah. GRASP - a new search algorithm for satisfiability. In Proceedings of the International Conference on Computer Aided Design, pages 220-227, San Jose, CA, November 1996. ACM/IEEE.
20. J. Pearl. Probablistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Mateo, CA, 1988.
21. D. Poole and N. L. Zhang. Exploiting contextual independence in probabilistic inference. Journal of Arti cial Intelligence Research, 18:263-313, 2003.
22. D. Roth. On the hardness of approximate reasoning. Arti cial Intelligence, 82(1/2):273-302, 1996.
23. T. Sang, F. Bacchus, P. Beame, H. Kautz, and T. Pitassi. Combining component caching and clause learning for effective model counting. In Seventh International Conference on Theory and Applications of Satis ability Testing, 2004.
24. T. Sang, P. Beame, and H. Kautz. Heuristics for fast exact model counting. To appear in SAT05, 2005.
25. M. Shwe, B. Middleton, D. Heckerman, M. Henrion, E. Horvitz, H. Lehmann, and G. Cooper. Probabilistic diagnosis using a reformulation of the internist- 1/qmr knowledge base i. the probabilistic model and inference algorithms. Methods of Information in Medicine, 30:241-255, 1991.
26. D. J. Spiegelhalter. Probabilistic reasoning in predictive expert systems. In L. N. Kanal and J. F. Lemmer, editors. Uncertainty in Arti cial Intelligence. Elsevier/North-Holland, 1986.
27. L. Zhang, C. F. Madigan, M. H. Moskewicz, and S. Malik. Efficient conflict driven learning in a boolean satisfiability solver. In Proceedings of the International Conference on Computer Aided Design, pages 279-285, San Jose, CA, November 2001. ACM/IEEE.