A Unified Theory of Heuristic Evaluation Functions and its Application to Learning

Jens Christensen, Richard E. Korf

We present a characterization of heuristic evaluation functions which unifies their treatment in single-agent problems and two-person games. The central result is that a useful heuristic function is one which determines the outcome of a search and is invariant along a solution path. This local characterization of heuristics can be used to predict the effectiveness of given heuristics and to automatically learn useful heuristic functions for problems. In one experiment, a set of relative weights for the different chess pieces was automatically learned.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.