Learning heuristic functions from relaxed plans

ICAPS 2006 - Proceedings, Sixteenth International Conference on Automated Planning and Scheduling

Volumn 2006, Issue , 2006, Pages 162-170

  Yoon, SungWook ^a   Fern, Alan ^b   Givan, Robert ^a  

^a Purdue University   (United States)

^b Oregon State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPETITIVE PLANNERS; GENERIC FEATURES; RELAXED PLANS (RP); TRAINING DATA;

ARTIFICIAL INTELLIGENCE; DATA ACQUISITION; DATABASE SYSTEMS; FUNCTIONS; PROBLEM SOLVING; STRATEGIC PLANNING;

HEURISTIC METHODS;

EID: 33746077092     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Blum, A., and Furst, M. 1995. Fast planning through planning graph analysis. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI 95), 1636-1642.
2. Bonet, B., and Geffner, H. 1999. Planning as heuristic search: New results. In ECP, 360-372.
3. Boyan, J., and Moore, A. 2000. Learning evaluation functions to improve optimization by local search. Journal of Machine Learning Research 1:77-112.
4. Breiman, L. 1996. Bagging predictors. Machine Learning 24(2):123-140.
5. Buro, M. 1998. From simple features to sophiscated evaluation functions. In van den Herik, H. J., and lida, H., eds., Proceedings of the First International Conference on Computers and Games ((CG)-98), volume 1558, 126-145. Tsukuba, Japan: Springer-Verlag.
6. Daume III, H., and Marcu, D. 2005. Learning as search optimization: Approximate large margin methods for structured prediction. In International Conference on Machine Learning (ICML).
7. Fern, A.; Yoon, S.; and Givan, R. 2004. Learning domain-specific control knowledge from random walks. In ICAPS.
8. Geffner, H. 2004. Planning graphs and knowledge compilation. In International Conference on Principles of Knowledge Representation and Reasoning.
9. Hoffmann, J., and Nebel, B. 2001. The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14:263-302.
10. Huang, Y.-C.; Selman, B.; and Kautz, H. 2000. Learning declarative control rules for constraint-based planning. In Proceedings of the 17th International Conference on Machine Learning, 415-422. Morgan Kaufmann, San Francisco, CA.
11. Ilghami, O.; Munoz-Avila, H.; Nau, D. S.; and Aha, D. W. 2005. Learning preconditions for planning from plan traces and htn structure. In International Conference on Machine Learning (ICML).
12. Khardon, R. 1999. Learning action strategies for planning domains. Artificial Intelligence 113(1-2):125-148.
13. Martin, M., and Geffner, H. 2000. Learning generalized policies in planning domains using concept languages. In Proceedings of the 7th International Conference on Knowledge Representation and Reasoning.
14. McAllester, D., and Givan, R. 1993. Taxonomic syntax for first-order inference. Journal of the ACM 40:246-283.
15. Minton, S., ed. 1993. Machine Learning Methods for Planning. Morgan Kaufmann Publishers.
16. Nguyen, X.; Kambhampati, S.; and Nigenda, R. S. 2002. Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search. Artificial Intelligence 135(1-2):73-123.
17. Vidal, V. 2004. A lookahead strategy for heuristic search planning. In International Conference on Automated Planning and Scheduling.
18. Yoon, S.; Fern, A.; and Givan, R. 2005. Learning measures of progress for planning domains. In Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence.
19. Zhang, W., and Dietterich, T. G. 1995. A reinforcement learning approach to job-shop scheduling. In Proceedings of the International Joint Conference on Artificial Intellience.