Learning symbolic models of stochastic domains

Journal of Artificial Intelligence Research

Volumn 29, Issue , 2007, Pages 309-352

  Pasula, Hanna M ^a   Zettlemoyer, Luke S ^a   Kaelbling, Leslie Pack ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTOMATA THEORY; LEARNING SYSTEMS; PROBABILISTIC LOGICS; THREE DIMENSIONAL;

LEARNING SYMBOLIC MODELS; NONDETERMINISTIC ACTION EFFECTS; RELATIONAL PLANNING RULES;

STOCHASTIC MODELS;

EID: 34748875246     PISSN: None     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.2113     Document Type: Article

References (36)

1. Agre, P., & Chapman, D. (1987). Pengi: An implementation of a theory of activity. In Proceedings of the Sixth National Conference on Artificial Intelligence (AAAI).
2. AIPS (2002). International planning competition, http://www.dur.ac.uk/d. p.long/competition.html.
3. AIPS (2006). International planning competition, http://www.ldc.usb.ve/ ~bonet/ipc5/.
4. Amir, E. (2005). Learning partially observable deterministic action models. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI).
5. Assche, A. V., Vens, C., Blockeel, H., & Džeroski, S. (2004). A random forest approach to relational learning. In Proceedings of the ICML Workshop on Statistical Relational Learning and its Connections to Other Fields.
6. Benson, S. (1996). Learning Action Models for Reactive Autonomous Agents. Ph.D. thesis, Stanford University.
7. Bertsekas, D. P. (1999). Nonlinear Programming. Athena Scientific.
8. Blum, A., & Langford, J. (1999). Probabilistic planning in the graphplan framework. In Proceedings of the Fifih European Conference on Planning (ECP).
9. Boutilier, C., Reiter, R., & Price, B. (2001). Symbolic dynamic programming for first-order MDPs. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI).
10. Boyen, X., & Koller, D. (1998). Tractable inference for complex stochastic processes. In Proceedings of the Fourteenth Annual Conference on Uncertainty in AI (UAI).
11. Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47.
12. Draper, D., Hanks, S., & Weld, D. (1994). Probabilistic planning with information gathering and contingent execution. In Proceedings of the Second International conference on AI Planning Systems (AIPS).
13. Edelkamp, S., & Hoffman, J. (2004). PDDL2.2: The language for the classical part of the 4th international planning competition. Technical Report 195, Albert-Ludwigs-Universität, Freiburg, Germany.
14. Fikes, R. E., & Nilsson, N. J. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(2).
15. Getoor, L. (2001). Learning Statistical Models From Relational Data. Ph.D. thesis, Stanford.
16. Gil, Y. (1993). Efficient domain-independent experimentation. In Proceedings of the Tenth International Conference on Machine Learning (ICML).
17. Gil, Y. (1994). Learning by experimentation: Incremental refinement of incomplete planning domains. In Proceedings of the Eleventh International Conference on Machine Learning (ICML).
18. Guestrin, C., Koller, D., Parr, R., & Venkataraman, S. (2003). Efficient solution algorithms for factored MDPs. Journal of Artificial Intelligence Research (JAIR), 19.
19. Kearns, M., Mansour, Y., & Ng, A. (2002). A sparse sampling algorithm for near-optimal planning in large Markov decision processes. Machine Learning (ML), 49(2).
20. Kersting, K. (2006). An Inductive Logic Programming Approach to Statistical Relational Learning. IOS Press.
21. Kersting, K., Raedt, L. D., & Raiko, T. (2006). Logical hidden markov models. Journal of Artificial Intelligence Research (JAIR), 25.
22. Khan, K., Muggleton, S., & Parson, R. (1998). Repeat learning using predicate invention. In International Workshop on Inductive Logic Programming (ILP).
23. Kok, S., & Domingos, P. (2005). Learning the structure of markov logic networks. In Proceedings of the Twenty Second International Conference on Machine Learning (ICML).
24. Lavrač, N., & Džeroski, S. (1994). Inductive Logic Programming Techniques and Applications. Ellis Horwood.
25. Mitchell, T. M. (1982). Generalization as search. Artificial Intelligence, 18(2).
26. Oates, T., & Cohen, P. R. (1996). Searching for planning operators with context-dependent and probabilistic effects. In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI).
27. ODE (2004). Open dynamics engine toolkit., http://opende.sourceforge.net.
28. Pasula, H., Zettlemoyer, L., & Kaelbling, L. (2004). Learning probabilistic relational planning rules. In Proceedings of the Fourteenth International Conference on Automated Planning and Scheduling (ICAPS).
29. Puterman, M. L. (1999). Markov Decision Processes. John Wiley and Sons, New York.
30. Richardson, M., & Domingos, P. (2006). Markov logic networks. Machine Learning (ML), 62.
31. Sanghai, S., Domingos, P., & Weld, D. (2005). Relational dynamic bayesian networks. Journal of Artificial Intelligence Research (JAIR), 24.
32. Shen, W.-M., & Simon, H. A. (1989). Rule creation and rule learning through environmental exploration. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI).
33. Wang, X. (1995). Learning by observation and practice: An incremental approach for planning operator acquisition. In Proceedings of the Twelfth International Conference on Machine Learning (ICML).
34. Yoon, S., Fern, A., & Givan, R. (2002). Inductive policy selection for first-order Markov decision processes. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI).
35. Younes, H. L. S., & Littman, M. L. (2004). PPDDL1.0: An extension to PDDL for expressing planning domains with probabilistic effects. School of Computer Science, Carnegie Mellon University, Technical Report CMU-CS-04-167.
36. Zettlemoyer, L., Pasula, H., & Kaelbling, L. (2003). Learning probabilistic relational planning rules. MIT Tech Report.