Sampling first order logical particles

Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008

Volumn , Issue , 2008, Pages 248-255

  Hajishirzi, Hannaneh ^a   Amir, Eyal ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE INFERENCE; COMPACT REPRESENTATION; DETERMINISTIC EXECUTION; EMPIRICAL RESULTS; FIRST ORDER; FIRST ORDER LOGIC; HIGH PRECISION; NATURAL LANGUAGE PROCESSING; PARTIAL OBSERVATION; PROBABILISTIC SEQUENCES; SAMPLING TECHNIQUE; SEQUENCE OF ACTIONS; SEQUENTIAL MONTE CARLO;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL LINGUISTICS; MONTE CARLO METHODS; NATURAL LANGUAGE PROCESSING SYSTEMS; ROBOT PROGRAMMING; UNCERTAINTY ANALYSIS;

ESTIMATION;

EID: 80053262851     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. F. Bacchus, J. Y. Halpern, and H. J. Levesque. Reasoning about noisy sensors and effectors in the situation calculus. AI, 1999.
2. C. Boutilier, R. Reiter, and B. Price. Symbolic dynamic programming for first-order MDPs. In IJCAI, 2001.
3. V. S. Costa, D. Page, M. Qazi, and J. Cussens. Clp(bn): Constraint logic programming for probabilistic knowledge. In UAI, 2003.
4. T. Dean and K. Kanazawa. Probabilistic temporal reasoning. In AAAI, 1988.
5. A. Doucet, N. de Freitas, and N. Gordon. Sequential Monte Carlo Methods in Practice. Springer, 1st edition, 2001.
6. A. Doucet, N. de Freitas, K. Murphy, and S. Russell. Raoblackwellised particle filtering for dynamic bayesian networks. In UAI, 2000.
7. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In IJCAI, 1999.
8. H. Hajishirzi and E. Amir. Stochastic filtering in a probabilistic action model. In AAAI'07, 2007.
9. Michael Jordan. Introduction to probabilistic graphical models. 2007. Forthcoming.
10. K. Kersting, L. D. Raedt, and T. Raiko. Logical hidden markov models. Artificial Intelligence Research, 2006.
11. U. Kjærulff. A computational scheme for reasoning in dynamic probabilistic networks. In UAI, 1992.
12. S. Muggleton. Stochastic logic programs. In Workshop on ILP, 1995.
13. Kevin Murphy. Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, 2002.
14. M. Nance, A. Vogel, and E. Amir. Reasoning about partially observed actions. In AAAI, 2006.
15. D. Pool. Probabilistic horn abduction and bayesian networks. Artificial Intelligence, 64:81-129, 1993.
16. L. R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. IEEE, 1989.
17. R. Reiter. Knowledge In Action. MIT Press, 2001.
18. Matthew Richardson and Pedro Domingos. Markov logic networks. Machine Learning, 2006.
19. A. Shirazi and E. Amir. First order logical filtering. In IJCAI, 2005.
20. B. Taskar, P. Abbeel, and D. Koller. Discriminative probabilistic models for relational data. In UAI'02, 2002.
21. M. Welling, I. Porteous, and E. Bart. Infinite state bayesian networks. In NIPS, 2007.
22. L. S. Zettlemoyer, H. M. Pasula, and L. P. Kaelbling. Logical particle filtering. In Dagstuhl Seminar on Probabilistic, Logical, and Relational Learning, 2007.