Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Searching with probabilities
On optimal game-tree search using rational meta-reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Pathology on game trees revisited, and an alternative to minimaxing
Artificial Intelligence
Logarithmic-time updates and queries in probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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In this paper we address the problem of making correct decisions in the context of game-playing. Specifically, we address the problem of reducing or eliminating pathology in game trees. However, the framework used in the paper applies to decision making that depends on evaluating complex Boolean expressions. The main contribution of this paper is in casting general evaluation of game trees as belief propagation in causal trees. This allows us to draw several theoretically and practically interesting corollaries. • In the Bayesian framework we typically do not want to ignore any evidence, even if it may be inaccurate. Therefore, we evaluate the game tree on several levels rather than just the deepest one. • Choosing the correct move in a game can be implemented in a straightforward fashion by an efficient linear-time algorithm adapted from the procedure for belief propagation in causal trees. • We propose a probabilistic ally sound heuristic that allows us to reduce the effects of pathology significantly.