A New Result on the Complexity of Heuristic Estimates for the A* Algorithm

Relaxed models are abstract problem descriptions generated by ignoring constraints that are present in base-level problems. They play an important role in planning and search algorithms, as it has been shown that the length of an optimal solution to a relaxed model yields a monotone heuristic for an A search of a base-level problem. Optimal solutions to a relaxed model may be computed algorithmically or by search in a further relaxed model, leading to a search that explores a hierarchy of relaxed models. In this paper, we review the traditional definition of problem relaxation and show that searching in the abstraction hierarchy created by problem relaxation will not reduce the computational effort required to find optimal solutions to the baselevel problem, unless the relaxed problem found in the hierarchy can be transformed by some optimization (e.g., subproblem factoring). Specifically, we prove that any A search of the base-level using a heuristic h2 will largely dominate an A ⋆ search ∗This paper was published in Artificial Intelligence, 55, 1 (May 1992), 129-143. This research was made possible by support from Heuristicrats Research Inc. and the National Aeronautics and Space Administration. The first two authors may be reached at {othar,mayer}@heuristicrat.com. The third author may be reached at [email protected]. of the base-level using a heuristic h1, if h1 must be computed by an A ⋆ search of the relaxed model using h2.