Hybrid markov logic networks

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2008, Pages 1106-1111

  Jue, Wang ^a   Domingos, Pedro ^a  

^a University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATIONS; ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; BIONICS; CLUSTERING ALGORITHMS; COMPUTATIONAL METHODS; CONFORMAL MAPPING; FORMAL LOGIC; INFERENCE ENGINES; WIRELESS NETWORKS;

COMPACT MODELING; CONTINUOUS DOMAINS; DO-MAINS; EXPONENTIAL FAMILIES; FIRST ORDERS; GAUSSIANS; INFERENCE ALGORITHMS; MARKOV LOGIC NETWORKS; MARKOV NETWORKS; MODELING LANGUAGES; ROBOT MAPPINGS; SAT ALGORITHMS;

KNOWLEDGE BASED SYSTEMS;

EID: 57749175299     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Bakir, G. H., Hofmann, T., Scholkopf, B., Smola, A. J., Taskar, B., and Vishwanathan, S. V. N. (2007). Predicting Structured Data. MIT Press.
2. Genesereth, M., and Nilsson, N. (1987). Logical Foundations of Artificial Intelligence. Morgan Kaufmann.
3. Getoor, L., and Taskar, B. (2007). Introduction to Statistical Relational Learning. MIT Press.
4. Gilks, W. R., Richardson, S., and Spiegelhalter, D. J., editors (1996). Markov Chain Monte Carlo in Practice. Chapman and Hall.
5. Kautz, H., Selman, B., and Jiang, Y. (1997). A general stochastic approach to solving problems with hard and soft constraints. In The Satisfiability Problem. AMS.
6. Kok, S., and Domingos, P. (2005). Learning the structure of Markov logic networks. In Proc. ICML-05 (pp. 441-448).
7. Lerner, U., and Parr, R. (2001). Inference in hybrid networks: Theoretical limits and practical algorithms. In Proc. UAI-01 (pp. 310-318).
8. Limketkai, B., Liao, L., and Fox, D. (2005). Relational object maps for mobile robots. In Proc. IJCAI-05 (pp. 1471-1476).
9. Liu, D. C., and Nocedal, J. (1989). On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45:503-528, 1989.
10. Lowd, D., and Domingos, P. (2007). Efficient weight learning for Markov logic networks. In Proc. PKDD-07 (pp. 200-211).
11. Marinari, E., and Parisi, G. (1992). Simulated tempering: a new Monte Carlo scheme. Europhysics Letters, 19, 451-458, 1992.
12. Murphy, K. (1998). Inference and learning in hybrid bayesian networks. Tech. Rept. UCB/CSD-98-990, U. C. Berkeley, CA.
13. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann.
14. Pietra, S. D., Pietra, V. D., and Lafferty, J. (1997). Inducing features of random fields. Trans. PAMI, 19, 380-393, 1997.
15. Poon, H., and Domingos, P. (2006). Sound and efficient inference with probabilistic and deterministic dependencies. In Proc. AAAI-06 (pp. 458-463).
16. Richardson, M., and Domingos, P. (2006). Markov logic networks. Machine Learning, 62, 107-136, 2006.
17. Selman, B., Kautz, H., and Cohen, B. (1996). Local search strategies for satisfiability testing. In Cliques, Coloring, and Satisfiability. AMS.
18. Wei, W., Erenrich, J., and Selman, B. (2004). Towards efficient sampling: Exploiting random walk strategies. In Proc. AAAI-04 (pp. 670-676).