Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Searching with probabilities
Comparison of the minimax and product back-up rules in a variety of games
Search in Artificial Intelligence
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
A Bayesian approach to relevance in game playing
Artificial Intelligence - Special issue on relevance
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
Bayesian pattern ranking for move prediction in the game of Go
ICML '06 Proceedings of the 23rd international conference on Machine learning
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We apply probability theory to the task of proving whether a goal can be achieved by a player in an adversarial game. Such problems are solved by searching the game tree. We view this tree as a graphical model which yields a distribution over the (Boolean) outcome of the search before it terminates. Experiments show that a best-first search algorithm guided by this distribution explores a similar number of nodes as Proof-Number Search to solve Go problems. Knowledge is incorporated into search by using domain-specific models to provide prior distributions over the values of leaf nodes of the game tree. These are surrogate for the unexplored parts of the tree. The parameters of these models can be learned from previous search trees. Experiments on Go show that the speed of problem solving can be increased by orders of magnitude by this technique but care must be taken to avoid over-fitting.