Information-Theoretic Advisors in Invisible Chess
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:29-34, 2001.
Making decisions under uncertainty remains a central problem in AI research. Unfortunately, most uncertain real-world problems are so complex that progress in them is extremely difficult. Games model some elements of the real world, and offer a more controlled environment for exploring methods for dealing with uncertainty. Chess and chesslike games have long been used as a strategically complex test-bed for general AI research, and we extend that tradition by introducing an imperfect information variant of chess with some useful properties such as the ability to scale the amount of uncertainty in the game. We discuss the complexity of this game which we call invisible chess, and present results outlining the basic game. We motivate and describe the implementation and application of two information-theoretic advisors, and describe our decision-theoretic approach to combining these information-theoretic advisors with a basic strategic advisor. Finally we discuss promising preliminary results that we have obtained with these advisors.