Toward Rational Deployment of Multiple Heuristics in A

The obvious way to use several admissible heuristics in A∗ is to take their maximum. In this paper we aim to reduce the time spent on computing heuristics. We discuss Lazy A∗, a variant of A∗ where heuristics are evaluated lazily: only when they are essential to a decision to be made in the A∗ search process. We present a new rational meta-reasoning based scheme, rational lazy A∗, which decides whether to compute the more expensive heuristics at all, based on a myopic value of information estimate. Both methods are examined theoretically. Empirical evaluation on several domains supports the theoretical results, and shows that lazy A∗ and rational lazy A∗ are state-of-the-art heuristic combination methods.