The Bench Transition System and Stochastic Exploration

Abstract

Stochastic exploration has been shown to be an effective way to mitigate the negative impact that heuristic local minima and plateaus can have on Greedy Best First Search (GBFS). Previous work has induced exploration using type systems, which typically partition the state-space using simple features like heuristic value and depth. In this work, we introduce new type systems motivated by the Bench Transition System (BTS). The BTS is a structure used to characterize the behaviour of GBFS, that is based on high water-mark benches, which are sets of states that have made the same amount of progress towards the goal. Since the BTS cannot be constructed during search, our type systems approximate the BTS using the notions of Heuristic Improvement and Low Water-Mark. We first identify that these approximations are exact in state-spaces with plateaus but no local minima, and also show that the resulting type systems are probabilistically complete. Our empirical evaluation shows the effectiveness of this approach on a variety of planning domains.