Using knowledge generated in heuristic search for nonchronological backtracking

Problem solvers that use heuristics to guide choices often run into untenable situations that can be characterized as overconstrained. When this happens, the problem must be able to identify the right culprit from among its heuristic choices in order to avoid a potentially explosive search. In this paper, we present a solution to this for a certain class of problems where the justifications associated with choice points involve an explicit assessment of the pros and cons of choosing each alternative relative to its competitors. We have designed a problem solver that accumulates such knowledge about the pros and cons of alternative selections at choice points during heuristic search, which it updates in light of an evolving problem situation. Whenever untenable situations arise, this preserved knowledge is used to determine the most appropriate backtracking point. By endowing the backtracker with access to this domain‐specific knowledge, a highly contextual approach to reasoning in backtracking situations can be achieved.