Book Description:
The mind-set that has dominated the history of computer game playing relies on straightforward exploitation of the available computing power. The fact that a machine can explore millions of variations sooner than the sluggish human can wink an eye has inspired hopes that the mystery of intelligence can be cracked, or at least side-stepped, by sheer force. Decades of the steadily growing strength of computer programs have attested to the soundness of this approach. It is clear that deeper understanding can cut the amount of necessary calculations by orders of magnitude.
The papers collected in this volume describe how to instill learning skills in game playing machines. The reader is asked to keep in mind that this is not just about games---the possibility that the discussed techniques will be used in control systems and in decision support always looms in the background.

Table of Contents:
Chapter 1: Should Machines Learn How to Play Games?; Chapter 2: Machine Learning in Games: A Survey; Chapter 3: Human Learning in Game Playing; Chapter 4: Toward Opening Book Learning; Chapter 5: Reinforcement Learning and Chess; Chapter 6: Comparison Training of Chess Evaluation Functions; Chapter 7: Feature Construction for Game Playing; Chapter 8: Learning to Play Expertly: A Tutorial on Hoyle; Chapter 9: Acquisition of Go Knowledge from Game Records; Chapter 10: Honte, a Go-Playing Program Using Neural Nets; Chapter 11: Learning to Play Strong Poker; Bibliography; Contributors; Person Index; Subject Index