Rational abstract search

Michael P. Frank
MIT Lab for Computer Science
Email: mpf@ai.mit.edu

Abstract

Computer game-playing (CGP) is potentially a good arena for the investigation of planning techniques. However, most CGP methods developed so far take a very limited view of planning and reasoning. Specifically, they focus on state-space search, to the exclusion of more abstract kinds of reasoning about the future.

In this paper, I motivate the use of abstraction in game-playing, describe various kinds of abstraction that might be useful in game-playing and planning, and show how they can all be subsumed under a unifying definition of abstractions. I propose a methodology for abstraction research that involves translating existing CGP methods into a language of abstraction networks before trying to generalize those methods to work with a variety of types of abstractions. I then summarize the results of my master's thesis work, in progress, on applying decision-theoretic techniques to do effective and efficient reasoning within the abstraction-network framework.

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