First order decision diagrams for relational MDPs

Journal of Artificial Intelligence Research

Volumn 31, Issue , 2008, Pages 431-472

  Wang, Chenggang ^a   Joshi, Saket ^a   Khardon, Roni ^a  

^a Tufts University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CASE BASED REASONING; DECISION MAKING; FUNCTION EVALUATION; OPTIMAL SYSTEMS; OPTIMIZATION; RELATIONAL DATABASE SYSTEMS;

DECISION DIAGRAMS; FIRST ORDER DECISION DIAGRAMS (FODD); RELATIONAL MARKOV DECISION PROCESSES (RMDP);

DECISION SUPPORT SYSTEMS;

EID: 44449127194     PISSN: None     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.2489     Document Type: Article

References (37)

1. Bahar, R. I., Frohm, E. A., Gaona, C. M., Hachtel, G. D., Macii, E., Pardo, A., & Somenzi, F. (1993). Algebraic decision diagrams and their applications. In Proceedings of the International Conference on Computer-Aided Design, pp. 188-191.
2. Bellman, R. E. (1957). Dynamic programming. Princeton University Press.
3. Blockeel, H., & De Raedt, L. (1998). Top down induction of first order logical decision trees. Artificial Intelligence, 101, 285-297.
4. Boutilier, C., Dean, T., & Goldszmidt, M. (2000). Stochastic dynamic programming with factored representations. Artificial Intelligence, 121(1), 49-107.
5. Boutilier, C., Dean, T., & Hanks, S. (1999). Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research, 11, 1-94.
6. Boutilier, C., Dearden, R., & Goldszmidt, M. (1995). Exploiting structure in policy construction. In Proceedings of the International Joint Conference of Artificial Intelligence, pp. 1104-1111.
7. Boutilier, C., Reiter, R., & Price, B. (2001). Symbolic dynamic programming for first-order MDPs. In Proceedings of the International Joint Conference of Artificial Intelligence, pp. 690-700.
8. Bryant, R. E. (1986). Graph-based algorithms for boolean function manipulation. IEEE Transactions on Computers, C-35(8), 677-691.
9. Bryant, R. E. (1992). Symbolic boolean manipulation with ordered binary decision diagrams. ACM Computing Surveys, 24(3), 293-318.
10. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001). Introduction to Algorithms. MIT Press.
11. Driessens, K., Ramon, J., & Gärtner, T. (2006). Graph kernels and gaussian processes for relational reinforcement learning. Machine Learning, 64(1-3), 91-119.
12. Dzeroski, S., De Raedt, L., & Driessens, K. (2001). Relational reinforcement learning. Machine Learning, 43, 7-52.
13. Feng, Z., & Hansen, E. A. (2002). Symbolic heuristic search for factored Markov Decision Processes. In Proceedings of the National Conference on Artificial Intelligence, pp. 455-460.
14. Fern, A., Yoon, S., & Givan, R. (2003). Approximate policy iteration with a policy language bias. In International Conference on Neural Information Processing Systems.
15. Fern, A., Yoon, S., & Givan, R. (2006). Approximate policy iteration with a policy language bias: Solving relational Markov Decision Processes. Journal of Artificial Intelligence Research, 25, 75-118.
16. Garriga, G., Khardon, R., & De Raedt, L. (2007). On mining closed sets in multi-relational data. In Proceedings of the International Joint Conference of Artificial Intelligence, pp. 804-809.
17. Gretton, C., & Thiebaux, S. (2004). Exploiting first-order regression in inductive policy selection. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp. 217-225.
18. Groote, J. F., & Tveretina, O. (2003). Binary decision diagrams for first-order predicate logic. The Journal of Logic and Algebraic Programming, 57, 1-22.
19. Großmann, A., Hölldobler, S., & Skvortsova, O. (2002). Symbolic dynamic programming within the fluent calculus. In Proceedings of the IASTED International Conference on Artificial and Computational Intelligence.
20. Guestrin, C., Koller, D., Gearhart, C., & Kanodia, N. (2003a). Generalizing plans to new environments in relational MDPs. In Proceedings of the International Joint Conference of Artificial Intelligence, pp. 1003-1010.
21. Guestrin, C., Koller, D., Par, R., & Venktaraman, S. (2003b). Efficient solution algorithms for factored MDPs. Journal of Artificial Intelligence Research, 19, 399-468.
22. Hansen, E. A., & Feng, Z. (2000). Dynamic programming for POMDPs using a factored state representation. In Proceedings of the International Conference on Artificial Intelligence Planning Systems, pp. 130-139.
23. Hoey, J., St-Aubin, R., Hu, A., & Boutilier, C. (1999). SPUDD: Stochastic planning using decision diagrams. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp. 279-288.
24. Höolldobler, S., Karabaev, E., & Skvortsova, O. (2006). FluCaP: a heuristic search planner for first-order MDPs. Journal of Artificial Intelligence Research, 27, 419-439.
25. Kersting, K., Otterlo, M. V., & De Raedt, L. (2004). Bellman goes relational. In Proceedings of the International Conference on Machine Learning.
26. McMillan, K. L. (1993). Symbolic model checking. Kluwer Academic Publishers.
27. Puterman, M. L. (1994). Markov decision processes: Discrete stochastic dynamic programming. Wiley.
28. Rivest, R. L. (1987). Learning decision lists. Machine Learning, 2(3), 229-246.
29. Sanghai, S., Domingos, P., & Weld, D. (2005). Relational dynamic bayesian networks. Journal of Artificial Intelligence Research, 24, 759-797.
30. Sanner, S., & Boutilier, C. (2005). Approximate linear programming for first-order MDPs. In Proceedings of the Conference on Uncertainty in Artificial Intelligence.
31. Sanner, S., & Boutilier, C. (2006). Practical linear value-approximation techniques for first-order MDPs. In Proceedings of the Conference on Uncertainty in Artificial Intelligence.
32. Sanner, S., & Boutilier, C. (2007). Approximate solution techniques for factored first-order MDPs. In Proceedings of the International Conference on Automated Planning and Scheduling.
33. Schuurmans, D., & Patrascu, R. (2001). Direct value approximation for factored MDPs. In International Conference on Neural Information Processing Systems, pp. 1579-1586.
34. St-Aubin, R., Hoey, J., & Boutilier, C. (2000). APRICODD: Approximate policy construction using decision diagrams. In International Conference on Neural Information Processing Systems, pp. 1089-1095.
35. Wang, C. (2007). First order Markov Decision Processes. Tech. rep. TR-2007-4, Computer Science Department, Tufts University.
36. Wang, C., Joshi, S., & Khardon, R. (2007). First order decision diagrams for relational MDPs. In Proceedings of the International Joint Conference of Artificial Intelligence, pp. 1095-1100.
37. Wang, C., & Khardon, R. (2007). Policy iteration for relational MDPs. In Proceedings of the Conference on Uncertainty in Artificial Intelligence.