Ensemble Monte-Carlo planning: An empirical study

ICAPS 2011 - Proceedings of the 21st International Conference on Automated Planning and Scheduling

Volumn , Issue , 2011, Pages 58-65

  Fern, Alan ^a   Lewis, Paul ^a  

^a OREGON STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION DOMAINS; EMPIRICAL STUDIES; MONTE CARLO; PER UNIT; PLANNING ALGORITHMS; SEARCH TREES; SINGLE SEARCH; SPACE AND TIME; TIME MODEL;

ALGORITHMS;

PLANT EXTRACTS;

EID: 80054833328     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Auer, P.; Cesa-Bianchi, N.; and Fischer, P. 2002. Finite-time analysis of the multiarmed bandit problem. Machine learning 47(2):235-256. (Pubitemid 34126111)
2. Balla, R., and Fern, A. 2009. UCT for tactical assault planning in real-time strategy games. In IJCAI, 40-45.
3. Bjarnason, R.; Fern, A.; and Tadepalli, P. 2009. Lower bounding Klondike solitaire with Monte-Carlo planning. In ICAPS, 26-33.
4. Breiman, L. 1996. Bagging predictors. Machine learning 24(2):123-140. (Pubitemid 126724382)
5. Breiman, L. 2001. Random forests. Machine learning 45:5-32. (Pubitemid 32933532)
6. Cazenave, T., and Jouandeau, N. 2007. On the parallelization of UCT. In Computer Games Workshop, 93-101.
7. Chaslot, G.; Winands, M.; and van den Herik, H. 2008. Parallel Monte-Carlo tree search. Computers and Games 60-71.
8. Dietterich, T. 2000. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine learning 40(2):139-157.
9. Finnsson, H., and Bjornsson, Y. 2008. Simulation-based approach to general game playing. In AAAI, 259-264.
10. Gelly, S., and Silver, D. 2007. Combining online and offline knowledge in UCT. In ICML.
11. Gelly, S.; Hoock, J.; Rimmel, A.; Teytaud, O.; and Kalemkarian, Y. 2008. On the parallelization of Monte-Carlo planning. In ICINCO.
12. Glenn, J. 2006. An optimal strategy for Yahtzee. Loyola College in Maryland, Tech. Rep. CS-TR-0002.
13. Kearns, M.; Mansour, Y.; and Ng, A. 2002. A sparse sampling algorithm for near-optimal planning in large Markov Decision Processes. Machine Learning 49:193-208. (Pubitemid 34325686)
14. Kocsis, L., and Szepesvari, C. 2006. Bandit based Monte-Carlo planning. In ECML, 282-293.
15. Lang, T., and Toussaint, M. 2010. Planning with Noisy Probabilistic Relational Rules. JAIR 39:1-49.
16. Tesauro, G. 1994. TD-Gammon, a self-teaching backgammon program, achieves master-level play. Neural computation 6(2):215-219.
17. Uiterwijk, J.; Van den Herik, H.; and Allis, L. 1989. A knowledge-based approach to connect-four: the game is over: white to move wins! University of Limburg.