A Framework for Quasi-exact Optimization Using Relaxed Best-First Search

Abstract. In this paper, a framework for previous and new quasi-exact extensions of the A -algorithm is presented. In contrast to previous approaches, the new methods guarantee to expand every state at most once if guided by a socalled monotone heuristic. By that, they account more effectively for aspects of run time while still guaranteeing that the cost of the solution will not exceed the optimal cost by a certain factor. First a general upper bound for this factor is derived. This bound is (1 + ) N 2 where N is (an upper bound on) the maximum depth of the search. Next, we look at specific instances of the algorithm class described by our framework. For one of the new methods a linear, i.e. much tighter upper bound is obtained: the cost of the solution will not exceed the optimal cost by a factor greater than 1 + . The parameter 0 can be chosen by the user. Within a range of reasonable choices for , all new methods allow the user to trade off run time for solution quality. Besides t...

• Type: Journal
• Year: 2006
• Where: KI
• Authors: Rüdiger Ebendt, Rolf Drechsler
• Keywords: Artificial Intelligence, General Upper Bound, KI 2006, Optimal Cost, Upper Bound