Partial order bounding: A new approach to evaluation in game tree search

Abstract

In computer game-playing, the established method for constructing an evaluation function uses a scalar value computed as a weighted sum of features. This paper advocates the use of partial order evaluation, and describes an efficient new search method called partial order bounding ( POB). Previous tree search algorithms using a partial order evaluation have attempted to propagate partially ordered values through the search tree, which leads to many problems in practice, such as the complexity of backing up sets of incomparable evaluations. POB compares partially ordered values only in the leaves of a game tree, and backs up boolean values through the tree. A closely related new algorithm, linear extension partial order bounding ( LE -POB ), uses a standard scalar alpha-beta search with values from a suitably chosen linear extension of the partial order evaluation. As an application, the effectiveness of partial order evaluation is shown in the case of modeling capturing races called semeai in the game of Go.