Incremental learning of relational action models in noisy environments

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6489 LNAI, Issue , 2011, Pages 206-213

  Rodrigues, Christophe ^a   Gérard, Pierre ^a   Rouveirol, Céline ^a  

^a UNIVERSITÉ PARIS 13   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION MODELS; DATA-DRIVEN; EMPIRICAL EVALUATIONS; FIRST ORDER; INCREMENTAL LEARNING; NOISY ENVIRONMENT; RELATIONAL REINFORCEMENT LEARNING; TRANSITION FUNCTIONS;

APPROXIMATION ALGORITHMS; LEARNING ALGORITHMS;

INDUCTIVE LOGIC PROGRAMMING (ILP);

EID: 79959308007     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21295-6_24     Document Type: Conference Paper

References (18)

1. Benson, S.: Inductive learning of reactive action models. In: ICML 1995, pp. 47-54 (1995)
2. Croonenborghs, T., Ramon, J., Blockeel, H., Bruynooghe, M.: Online learning and exploiting relational models in reinforcement learning. In: IJCAI, pp. 726-731 (2007)
3. Driessens, K., Ramon, J.: Relational instance based regression for relational reinforcement learning. In: ICML, pp. 123-130 (2003)
4. Driessens, K., Ramon, J., Blockeel, H.: Speeding up relational reinforcement learning through the use of an incremental first order decision tree algorithm. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 97-108. Springer, Heidelberg (2001) (Pubitemid 33331061)
5. Dzeroski, S., De Raedt, L., Driessens, K.: Relational reinforcement learning. Machine Learning 43, 7-52 (2001) (Pubitemid 32286614)
6. Esposito, F., Semeraro, G., Fanizzi, N., Ferilli, S.: Multistrategy theory revision: Induction and abduction in INTHELEX. Machine Learning 38(1-2), 133-156 (2000) (Pubitemid 30576118)
7. Gil, Y.: Learning by experimentation: Incremental refinement of incomplete planning domains. In: ICML, pp. 87-95 (1994)
8. Li, L., Littman, M.L., Walsh, T.J.: Knows what it knows: a framework for self-aware learning. In: ICML, pp. 568-575 (2008)
9. Pasula, H.M., Zettlemoyer, L.S.: Kaelbling L. Learning symbolic models of stochastic domains. Journal of Artificial Intelligence Research (JAIR) 29, 309-352 (2007) (Pubitemid 47477595)
10. Pasula, H.M., Zettlemoyer, L.S., Pack Kaelbling, L.: Learning probabilistic planning rules. In: ICAPS, pp. 146-163 (2004)
11. Rodrigues, C., Gérard, P., Rouveirol, C., Soldano, H.: Incremental learning of relational action rules. In: ICMLA. IEEE Computer Society, Los Alamitos (2010) (to appear)
12. Shen, W.M.: Discovery as autonomous learning from the environment. Machine Learning 12(1-3), 143-165 (1993)
13. Sutton, R.S.: Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In: ICML, pp. 216-224 (1990)
14. Van Otterlo, M.: The logic of adaptive behavior. PhD thesis, University of Twente, Enschede (2008)
15. Walsh, T.J., Littman, M.L.: Efficient learning of action schemas and web-service descriptions. In: AAAI, pp. 714-719 (2008)
16. Walsh, T.J., Szita, I., Diuk, M., Littman, M.L.: Exploring compact reinforcement-learning representations with linear regression. In: UAI, pp. 714-719 (2009)
17. Wang, X.: Learning by observation and practice: An incremental approach for planning operator acquisition. In: ICML, pp. 549-557 (1995)
18. Yang, Q., Wu, K., Jiang, Y.: Learning action models from plan examples using weighted max-sat. Artificial Intelligence 171(2-3), 107-143 (2007) (Pubitemid 46343538)