Title: A Theory of Heuristic Information in Game-Tree Search
Description: This book presents the use of imperfect information (called heuristic information) in game-tree search. Its purpose is to investigate the theoretical background of the use of heuristic information in game-tree search. Computer programs playing games usually search the game-tree to a reasonable depth with a static evaluation function and make decisions based upon backed-up values. Since the information in either the backed-up values or the values returned directly by the static evaluation function is often imperfect, decision making is usually not optimal. Also, pathological cases show why intuition about game-tree search is not always correct. This book introduces a mathematical formulation of heuristic information and a theoretical model of game-tree search. In this model, notions of game-tree search are formulated in mathematical terms and a sound mathematical theory of heuristic information is developed. The conventional pathological cases disappear in this theory. This book is accessible to the general AI community, for example first year graduate students who have completed an introductory AI course and have at least some background in probability. The book is also a foundation for further work on game-tree search as well as on heuristic information in general AI.
Subjects: Distribution (Probability theory), Statistics, Computer science, Artificial intelligence