Bellman goes relational

Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Volumn , Issue , 2004, Pages 465-472

  Kersting, Kristian ^a   Van Otterlo, Martijn ^a,b   De Raedt, Luc ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

^b UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; COMPUTER PROGRAMMING LANGUAGES; DECISION MAKING; FUNCTIONS; LEARNING SYSTEMS; MARKOV PROCESSES; MATHEMATICAL MODELS;

MARKOV DECISION PROCESSES (MDP); REBEL; REINFORCEMENT LEARNING (RL); VALUE ITERATION (VI);

MATHEMATICAL OPERATORS;

EID: 14344249892     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Bellman, R. E. (1957). Dynamic programming. Princeton, New Jersey: Princeton University Press.
2. Boutilier, C., Dean, T., & Hanks, S. (1999). Decisiontheoretic planning: Structural assumptions and computational leverage. J. Art. Intel. Res., 11, 1-94.
3. Boutilier, C., Reiter, R., & Price, B. (2001). Symbolic dynamic programming for first-order MDP's. Proc. of IJCAI'01.
4. Buntine, W. (1988). Generalized subumption and its applications to induction and redundancy. Artificial Intelligence, 36, 149-176.
5. Dietterich, T. G., & Flann, N. S. (1997). Explanation-based learning and reinforcement learning: a unified view. Machine Learning, 28, 169-210.
6. Driessens, K., & Ramon, J. (2003). Relational instance based regression for relational reinforcement learning. Proc. of ICML-2003.
7. Džeroski, S., De Raedt, L., & Driessens, K. (2001). Relational reinforcement learning. Machine Learning, 43, 7-52.
8. Fern, A., Yoon, S., & Givan, R. (2003). Approximate policy iteration with a policy language bias. Proc. of NIPS'03.
9. Frühwirth, T. (1998). Theory and Practice of Constraint Handling Rules. Journal of Logic Programming, 37, 95-138.
10. Gärtner, T., Driessens, K., & Ramon, J. (2003). Graph kernels and Gaussian processes for relational reinforcement learning. Proc. of ILP'03.
11. Guestrin, C., Koller, D., Gearhart, C., &: Kanodia, N. (2003). Generalizing plans to new environments in relational MDPs. Proc. of IJCAI'03.
12. Kersting, K., & De Raedt, L. (2003). Logical Markov decision programs. Proc. of the IJCAI'03 Workshop on Learning Statistical Models of Relational Data.
13. Lecoeuche, R. (2001). Learning optimal dialogue management rules by using reinforcement learning and inductive logic programming. Proc. of the North American Chapter of the Association for Computational Linguistics (NAACL). Pittsburgh.
14. Nienhuys-Cheng, S.-H., & de Wolf, R. (1997). Foundations of inductive logic programming, vol. 1228 of Lecture Notes in Artificial Intelligence. Springer-Verlag.
15. Sutton, R., &: Barto, A. (1998). Reinforcement learning: an introduction. Cambridge: The MIT Press.
16. Van Otterlo, M. (2004). Reinforcement learning for relational MDPs. Machine Learning Conference of Belgium and the Netherlands (BeNeLearn'04).