Learning probabilistic relational planning rules

Proceedings of the 14th International Conference on Automated Planning and Scheduling, ICAPS 2004

Volumn , Issue , 2004, Pages 73-81

  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

PLANNING REPRESENTATION; PROBABILISTIC PLANNING; PROBABILISTIC RELATIONAL RULES; REPRESENTATION LANGUAGE;

ALGORITHMS; INTELLIGENT AGENTS; KNOWLEDGE REPRESENTATION; PLANNING; PROBLEM SOLVING; ROBOT PROGRAMMING; SET THEORY;

LEARNING SYSTEMS;

EID: 13444256714     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. AIPS (2002). International planning competition.. http://www.dur.ac.uk/d. p.long/competition.html.
2. Benson, S. (1996). Learning Action Models for Reactive Autonomous Agents. Ph.D. thesis, Stanford University.
3. Bertsekas, D. P. (1999). Nonlinear Programming. Athena Scientific.
4. Blocked, H., & De Raedt, L. (1998). Top-down induction of first-order logical decision trees. Artificial Intelligence, 101(1-2).
5. Blum, A., & Langford, J. (1999). Probabilistic planning in the graphplan framework. In Proceedings of the Fifth European Conference on Planning.
6. Boutilier, C., Dearden, R., & Goldszmidt, M. (2002). Stochastic dynamic programming with factored representations. Artificial Intelligence, 121(1-2).
7. 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.
8. Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47.
9. Draper, D., Hanks, S., & Weld, D. (1994). Probabilistic planning with information gathering and contingent exe cution. In Proceedings of the Second International Conference on AI Planning and Scheduling.
10. Fikes, R. E., & Nilsson, N. J. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(2).
11. Friedman, N., & Goldszmidt, M. (1998). Learning Bayesian networks with local structure. In Learning and Inference in Graphical Models.
12. Getoor, L. (2001). Learning Statistical Models From Relational Data. Ph.D. thesis, Stanford University.
13. Gil, Y. (1993). Efficient domain-independent experimentation. In Proceedings of the Tenth International Conference on Machine Learning.
14. Gil, Y. (1994). Learning by experimentation: Incremental refinement of incomplete planning domains. In Proceedings of the Eleventh International Conference on Machine Learning.
15. Lavrač, N., & Džeroski, S. (1994). Inductive Logic Programming Techniques and Applications. Ellis Horwood.
16. 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.
17. Penberthy, J. S., & Weld, D. (1992). UCPOP: A sound, complete partial-order planner for ADL. In Proceedings of the Third International Conference on Knowledge Representation and Reasoning.
18. Plotkin, G. (1970). A note on inductive generalization. Machine Intelligence, 5.
19. 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.
20. Zettlemoyer, L., Pasula, H., & Kaelbling, L. (2003). Learning probabilistic relational planning rules. MIT CSAIL Technical Report.