Weighted A* Search - Unifying View and Application

The A* algorithm is a well-known heuristic best-first search method. Several performance-accelerated extensions of the exact A* approach are known. Interesting examples are approximate algorithms where the heuristic function used is inflated by a weight (often referred to as weighted A*). These methods guarantee a bounded suboptimality. As a technical contribution, this paper presents the previous results related to weighted A* from authors like Pohl, Pearl, Kim, Likhachev and others in a more condensed and unifying form. By use of this unified view, a novel general bound on suboptimality of the result is derived: in the case of avoiding any reopening of expanded states, for �µ>0, this bound is (1+�µ)^â��N/2â�� where N is (an upper bound on) the maximum depth of the search. Binary Decision Diagrams (BDDs) are well-known to AI, e.g. from set-based exploration of sparse-memory and symbolic manipulation of state spaces. The problem of exact or approximate BDD minimization is introduced as a possible new challenge for heuristic search. Like many classical AI domains, this problem is motivated by real-world applications. Several variants of weighted A* search are applied to problems of BDD minimization and the more classical domains like blockworlds and sliding-tile puzzles. For BDD minimization, the comparison of the evaluated methods also includes previous heuristic and simulation-based methods such as Rudell's hill-climbing based sifting algorithm, Simulated Annealing and Evolutionary Algorithms. A discussion of the results obtained in the different problem domains gives our experiences with weighted A*, which is of value for the AI practitioner