A Heuristic Estimator for Means-Ends Analysis in Planning

Proceedings Of The Third Artificial Intelligence Planning Systems Conference

Volume

Proceedings Of The Third Artificial Intelligence Planning Systems Conference

Contents

Means-ends analysis is a seemingly well understood search technique, which can be described, using planning terminology, as: keep adding actions that are feasible and achieve pieces of the goal. Unfortunately, it is often the case that no action is both feasible and relevant in this sense. The traditional answer is to make subgoals out of the preconditions of relevant but infeasible actions. These subgoals become part of the search state. An alternative, surprisingly good, idea is to recompute the entire subgoal hierarchy after every action. This hierarchy is represented by agreedy regression-match graph. The actions near the leaves of this graph are feasible and relevant to a sub. . . subgoals of the original goal. Furthermore, each subgoal is assigned an estimate of the number of actions required to achieve it. This number can be shown in practice to be a useful heuristic estimator for domains that are otherwise intractable.