Uncertainty in Artificial Intelligence
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Understanding Sampling Style Adversarial Search Methods
Raghuram Ramanujan, Ashish Sabharwal, Bart Selman
Abstract:
UCT has recently emerged as an exciting new adversarial reasoning technique based on cleverly balancing exploration and exploitation in a Monte-Carlo sampling setting. It has been particularly successful in the game of Go but the reasons for its success are not well understood and attempts to replicate its success in other domains such as Chess have failed. We provide an in-depth analysis of the potential of UCT in domain-independent settings, in cases where heuristic values are available, and the effect of enhancing random playouts to more informed playouts between two weak minimax players. To provide further insights, we develop synthetic game tree instances and discuss interesting properties of UCT, both empirically and analytically.
Keywords:
Pages: 474-483
PS Link:
PDF Link: /papers/10/p474-ramanujan.pdf
BibTex:
@INPROCEEDINGS{Ramanujan10,
AUTHOR = "Raghuram Ramanujan and Ashish Sabharwal and Bart Selman",
TITLE = "Understanding Sampling Style Adversarial Search Methods",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "474--483"
}


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