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.
1 Prelude: Why Study Games?
2 Overview: What Lies Ahead?
3 Background: What is Already Known?
3.2 Heuristic Evaluation Functions
3.3 Control Strategies
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
- No. of pages:
- © Morgan Kaufmann 1991
- 1st January 1991
- Morgan Kaufmann
- eBook ISBN: