HEURISTIC SEARCH UNDER CONTRACT

Abstract

In this article, we present a heuristic search technique (Contract Search) that can be adapted automatically for a specific node contract. We analyze the node expansion characteristics of best-first search techniques and identify a probabilistic model (rank profiles) that characterizes the search under restricted expansions. We use the model to formulate an optimal strategy to choose level dependent restriction bounds, maximizing the probability of obtaining the optimal cost goal node under the specified contract. We analyze the basic properties of the rank profiles and establish its relation with the search space configuration and heuristic error distributions. We suggest an approximation scheme for the profile function for unknown search spaces. We show how the basic framework can be adapted to achieve different objectives (like optimizing the expected quality) considering multiple goals and approximate solutions. Experimental comparison with anytime search techniques like ARA* and beam search on a number of search problems shows that Contract Search outperforms these techniques over a range of contract specifications.