Specification and Analysis of Real-Time Problem Solvers
IEEE Transactions on Software Engineering
Execution architectures and compilation
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Minimizing response times in real time planning and search
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
On optimal game-tree search using rational meta-reasoning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Can real-time search algorithms meet deadlines?
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Probability estimation in face of irrelevant information
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
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In this paper we outline a general approach to the study of problem-solving, in which search steps are considered decisions in the same sense as actions in the world. Unlike other metrics in the literature, the value of a search step is defined as a real utility rather than as a quasi-utility, and can therefore be computed directly from a model of the base-level problem-solver. We develop a formula for the value of a search step in a game-playing context using the single-step assumption, namely that a computation step can be evaluated as it was the last to be taken. We prove some meta-level theorems that enable the development of a low- overhead algorithm, MGSS, that chooses search steps in order of highest estimated utility. Although we show that the single-step assumption is untenable in general, a program implemented for the game of Othello appears to rival an alpha-beta search with equal node allocations or time allocations. Pruning and search termination subsumes or improve on many other algorithms. Single-agent search, as in the A algorithm, yields a simpler analysis, and we are currently investigating applications of the algorithm developed for this case.