Combining logical and probabilistic reasoning

AAAI Spring Symposium - Technical Report

Volumn SS-06-05, Issue , 2006, Pages 50-55

  Gelfond, Michael ^a   Rushton, Nelson ^a   Zhu, Weijun ^a  

^a Department of Electrical and Computer Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOMATION; COMPUTER PROGRAMMING LANGUAGES; PROBABILISTIC LOGICS; PROBLEM SOLVING;

P-LOG IMPLEMENTATION; PROBABILISTIC LOGIC LANGUAGES; PROBABILISTIC REASONING; QUANTITATIVE PROBABILITIES;

KNOWLEDGE BASED SYSTEMS;

EID: 33747154765     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Balduccini, M.; and Gelfond, M. 2003. Logic programs with consistency-restoring rules. In AAAI Spring 2003 Symposium, 9-18.
2. Baral, C. 2003. Knowledge representation, reasoning and declarative problem solving. Cambridge University Press.
3. Baral, C.; Gelfond, M.; and Rushton, N. 2004. Probabilistic reasoning with answer sets. In Proc. LRNMR-07.
4. Chavira, M.; Darwiche, A.; and Jaeger, M. 2004. Compiling relational Bayesian networks for exact inference. Proceedings of the Second European Workshop on Probabilistic Graphical Models(PGM-2004), The Netherlands, 49-56.
5. Gelfond, M.; and Lifschitz, V. 1988. The stable model semantics for logic programming. R.Kowalski, K. Bowen(Eds.), Logic Programming: Proc. Fifth Internat. Conference and Symposium, 1070-1080.
6. Iwan, G.; and Lakemeyer, G. 2002. What observations really tell us. In Cog Rob0́2.
7. Jaeger, M. 1997. Relational Bayesian networks. UAI97.
8. Niemelä, I.; and Simons, P. 1997. Smodels - an implementation of the stable model and well-founded semantics for normal logic programs. In Dix, J, Furbach, U, and Nerode, A, editors, Proc. 4th international conference on Logic programming and non-monotonic reasoning, 420-429. Springer.
9. Pearl, J. 2000. Causality: models, reasoning and inference. Cambridge University Press.
10. Simons, P. 1999. Extending the stable models semantics with more expressive rules. In Proc. LPNMR-99, Lecture Notes in Artificial Intelligence(LNCS). Springer Verlag, Berlin.