Search methods using heuristic strategies
Michael Georgeff · International Joint Conference on Artificial Intelligence · 1981
Real-valued heuristic functions have been extensively used as a means for constraining search in large problem spaces. In this paper we look at an alternative approach, called strategic search, In which heuristic information is expressed as strategies. Strategic search generates a search graph by following some strategy or set of strategies, backtracking to previous choice points when the current strategy falls. We first examine algorithms for performing strategic search using both deterministic and nondeterministic strategies. Some examples are given which indicate that strategic search can outperform standard heuristic search methods. The construction of strategies is also considered, and reans for acquiring strategic information both from analagous problems and from example execution traces are described. Finally, we indicate how meta-level strategies can be used to guide the application of object level strategies, thus providing a hierarchy of strategic information.