PROST: Probabilistic planning based on UCT

ICAPS 2012 - Proceedings of the 22nd International Conference on Automated Planning and Scheduling

Volumn , Issue , 2012, Pages 119-127

  Keller, Thomas ^a   Eyerich, Patrick ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DOMAINS; BRANCHING FACTORS; DEAD-ENDS; FASTER CONVERGENCE; PROBABILISTIC PLANNING; Q-VALUES; RANDOM WALK; SEARCH SPACES; SEARCH TREES;

ALGORITHMS;

EID: 84866455160     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Auer, P.; Cesa-Bianchi, N.; and Fischer, P. 2002. Finite-time Analysis of the Multiarmed Bandit Problem. Machine Learning 47:235-256.
2. Barto, A. G.; Bradtke, S. J.; and Singh, S. P. 1995. Learning to Act Using Real-Time Dynamic Programming. Artificial Intelligence (AIJ) 72(1-2):81-138.
3. Bellman, R. 1957. Dynamic Programming. Princeton University Press.
4. Bertsekas, D., and Tsitsiklis, J. 1996. Neuro-Dynamic Programming. Athena Scientific.
5. Bertsekas, D. 1995. Dynamic Programming and Optimal Control. Athena Scientific.
6. Bjarnason, R.; Fern, A.; and Tadepalli, P. 2009. Lower Bounding Klondike Solitaire with Monte-Carlo Planning. In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS), 26-33.
7. Bonet, B., and Geffner, H. 2003. Labeled RTDP: Improving the Convergence of Real-Time Dynamic Programming. In Proceedings of the 13th International Conference on Automated Planning and Scheduling (ICAPS), 12-31.
8. Boutilier, C.; Dearden, R.; and Goldszmidt, M. 2000. Stochastic Dynamic Programming with Factored Representations. Artificial Intelligence (AIJ) 121 (1-2):49-107.
9. Bylander, T. 1994. The Computational Complexity of Propositional STRIPS Planning. Artificial Intelligence (AIJ) 69:165-204.
10. Chakraborty, D., and Stone, P. 2011. Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function's In-Degree. In Proceedings of the 28th International Conference on Machine Learning (ICML), 737-744.
11. Degris, T.; Sigaud, O.; and Wuillemin, P.-H. 2006. Learning the Structure of Factored Markov Decision Processes in Reinforcement Learning Problems. In Proceedings of the 23rd International Conference on Machine Learning (ICML), 257-264.
12. Eyerich, P.; Keller, T.; and Helmert, M. 2010. High-Quality Policies for the Canadian Traveler's Problem. In Proceedings of the 24th Conference on Artificial Intelligence (AAAI), 51-58.
13. Finnsson, H., and Björnsson, Y. 2010. Learning Simulation Control in General Game-Playing Agents. In Proceedings of the 23rd Conference on Artificial Intelligence (AAAI), 954-959.
14. Finnsson, H., and Björnsson, Y. 2011. CadiaPlayer: Search Control Techniques. KI Journal 25(1):9-16.
15. Gelly, S., and Silver, D. 2007. Combining Online and Offline Knowledge in UCT. In Proceedings of the 24th International Conference on Machine Learning (ICML), 273-280.
16. Gelly, S., and Silver, D. 2011. Monte-Carlo Tree Search and Rapid Action Value Estimation in Ccomputer Go. Artificial Intelligence (AIJ) 175:1856-1875.
17. Guestrin, C.; Koller, D.; Parr, R.; and Venkataraman, S. 2003. Efficient Solution Algorithms for Factored MDPs. Journal of Artificial Intelligence Research (JAIR) 19:399-468.
18. Helmert, M., and Domshlak, C. 2009. Landmarks, Critical Paths and Abstractions: What's the Difference Anyway? In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS).
19. Keller, T., and Eyerich, P. 2011. A Polynomial All Outcomes Determinization for Probabilistic Planning. In Proceedings of the 21st International Conference on Automated Planning and Scheduling (ICAPS), 331-334. AAAI Press.
20. Kleene, S. C. 1950. Introduction to Metamathematics.
21. Kocsis, L., and Szepesvári, C. 2006. Bandit Based Monte-Carlo Planning. In Proceedings of the 17th European Conference on Machine Learning (ECML), 282-293.
22. Kolobov, A.; Dai, P.; Mausam; and Weld, D. 2012. Reverse Iterative Deepening for Finite-Horizon MDPs with Large Branching Factors. In Proceedings of the 22nd International Conference on Automated Planning and Scheduling (ICAPS). To Appear.
23. Little, I., and Thiébaux, S. 2007. Probabilistic Planning vs Replanning. In ICAPS Workshop International Planning Competition: Past, Present and Future.
24. Puterman, M. 1994. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley.
25. Sanner, S. 2010. Relational Dynamic Influence Diagram Language (RDDL): Language Description.
26. Teichteil-Königsbuch, F.; Infantes, G.; and Kuter, U. 2008. RFF: A Robust, FF-Based MDP Planning Algorithm for Generating Policies with Low Probability of Failure. In Proceedings of the 6th IPC at ICAPS.
27. Yoon, S. W.; Fern, A.; and Givan, R. 2007. FF-Replan: A Baseline for Probabilistic Planning. In Proceedings of the 17th International Conference on Automated Planning and Scheduling (ICAPS), 352-360.