Hierarchical strategy learning with hybrid representations

AAAI Workshop - Technical Report

Volumn WS-07-02, Issue , 2007, Pages 52-56

  Yoon, Sungwook ^a   Kambhampati, Subbarao ^a  

^a Arizona State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HIERARCHICAL REPRESENTATION; NOVICE LEARNERS; PLANNING ALGORITHMS; STRATEGIC LEVELS; STRATEGY LEARNING; SUPERIOR PERFORMANCE; TECHNICAL REPORTS; VALUE FUNCTIONS;

DECISION MAKING; LEARNING ALGORITHMS; PROBLEM SOLVING;

KNOWLEDGE REPRESENTATION;

EID: 51849088509     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Abbeel, P., and Ng, A. Y. 2004. Apprenticeship learning via inverse reinforcement learning. In Proc. ICML.
2. Andre, D., and Russell, S. 2000. Programmable reinforcement learning agents.
3. Cendrowska, J. 1987. Prism: An algorithm for inducing modular rules. International Journal of Man-Machine Studies 27(4):349-370.
4. Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; and Kegelmeyer, W. P. 2002. Smote: Synthetic minority oversampling technique.
5. Dietterich, T. G. 2000. Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research 13:227-303.
6. Erol, K.; Hendler, J. A.; and Nau, D. S. 1996. Complexity results for HTN planning. Annals of Mathematics and Artificial Intelligence 18(1):69-93.
7. Fern, A.; Yoon, S.; and Givan, R. 2003. Approximate policy iteration with a policy language bias. In Proceedings of the I6th Conference on Advances in Neural Information Processing.
8. Ilghami, O.; Nau, D. S.; and oz Avila, H. M. 2002. Camel: Learning method preconditions for htn planning. In AIPS02.
9. Kveton, B.; Hauskrecht, M.; and Guestrin, C. 2006. Solving factored MDPs with hybrid state and action variables. Journal of Artificial Intelligence Research 27:153-201.
10. Maloof, M. 2003. Learning when data sets are imbalanced and when costs are unequal and unknown.
11. McAllester, D., and Givan, R. 1993. Taxonomie syntax for first-order inference. Journal of the ACM 40:246-283.
12. Morales, E., and Sammut, C. 2004. Learning to fly by combining reinforcement learning with behavioural cloning. In ICML.
13. Nau, D.; Cao, Y.; Lotem, A.; and Munoz-Avila, H. 1999. Shop: Simple hierarchical ordered planner. In Proceedings of the International Joint Conference on Artificial Intelligence, 968-973.
14. Parr, R., and Russell, S. 1997. Reinforcement learning with hierarchies of machines. In Jordan, M. I.; Kearns, M. J.; and Solla, S. A., eds., Advances in Neural Information Processing Systems, volume 10. The MIT Press.
15. Phua, C.; Alahakoon, D.; and Lee, V. 2004. Minority report in fraud detection: classification of skewed data. SIGKDD Explor. Newsl. 6(1):50-59.
16. Precup, D., and Sutton, R. S. 1998. Multi-time models for temporally abstract planning. In Jordan, M. I.; Kearns, M. J.; and Solla, S. A., eds., Advances in Neural Information Processing Systems, volume 10. The MIT Press.
17. Yoon, S.; Fern, A.; and Givan, R. 2002. Inductive policy selection for first-order MDPs. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence.
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. Younes, H.; Musliner, D.; and Simmons, R. 2003. A framework for planning in continuous-time stochastic domains.