Fine-Grained Decision-Theoretic Search Control

arXiv.org Artificial Intelligence

Decision-theoretic control of search has previously used as its basic unit. of computation the generation and evaluation of a complete set of successors. Although this simplifies analysis, it results in some lost opportunities for pruning and satisficing. This paper therefore extends the analysis of the value of computation to cover individual successor evaluations. The analytic techniques used may prove useful for control of reasoning in more general settings. A formula is developed for the expected value of a node, k of whose n successors have been evaluated. This formula is used to estimate the value of expanding further successors, using a general formula for the value of a computation in game-playing developed in earlier work. We exhibit an improved version of the MGSS* algorithm, giving empirical results for the game of Othello.

A world-championship-level Othello program


Available for a fee.Manuscript available at Carnegie Mellon University.Othello is a recent addition to the collection of games that have been examined within artificial intelligence. Advances have been rapid, yielding programs that have reached the level of world-championship play. This article describes the current champion Othello program, Iago. The work described here includes: (1) a task analysis of Othello; (2) the implemenation of a program based on this analysis and state-of-the-art AI gameplaying techniques; and (3) an evaluation of the program's performance through games played against other programs and comparisons with expert human play.Artificial Intelligence, 19, 279- 320