The Expected-Outcome Model of Two-Player Games deals with the expected-outcome model of two-player games, in which the relative merit of game-tree nodes, rather than board positions, is considered. The ambiguity of static evaluation and the problems it generates in the search system are examined and the development of a domain-independent static evaluator is described. Comprised of eight chapters, this book begins with an overview of the rationale for the mathematical study of games, followed by a discussion on some previous artificial intelligence (AI) research efforts on game-trees. The next section opens with the definition of a node's expected-outcome value as the expected value of the leaves beneath it. The expected-outcome model is outlined, paying particular attention to the expected-outcome value of a game-tree node. This model was implemented on some small versions of tic-tac-toe and Othello. The book also presents results that offer strong support for both the validity of the expected-outcome model and the rationality of its underlying assumptions. This monograph is intended for specialists in AI and computer science.
Table of Contents
I Introduction 1 Prelude: Why Study Games? 2 Overview: What Lies Ahead? 3 Background: What is Already Known? 3.1 Preliminaries 3.2 Heuristic Evaluation Functions 3.3 Control Strategies 3.4 Summary II The Model 4 Proposal: What is the Basic Model? 5 Support: Does the Model Work? 5.1 Analytical Evidence 5.2 Empirical Evidence III Conclusions 75 6 Contributions: What's been Accomplished? 7 Implications: Where Might the Model Lead? 8 Reprise: Why Study Games? A Standard Evaluation Functions B The Random Sampler