Potential-based bounded-cost search and Anytime Non-Parametric A⁎A⁎

This paper presents two new search algorithms: Potential Search (PTS) and Anytime Potential Search/Anytime Non-Parametric A⁎A⁎ (APTS/ANA⁎APTS/ANA⁎). Both algorithms are based on a new evaluation function that is easy to implement and does not require user-tuned parameters. PTS is designed to solve bounded-cost search problems, which are problems where the task is to find as fast as possible a solution under a given cost bound. APTS/ANA⁎APTS/ANA⁎ is a non-parametric anytime search algorithm discovered independently by two research groups via two very different derivations. In this paper, co-authored by researchers from both groups, we present these derivations: as a sequence of calls to PTS and as a non-parametric greedy variant of Anytime Repairing A⁎A⁎.We describe experiments that evaluate the new algorithms in the 15-puzzle, KPP-COM, robot motion planning, gridworld navigation, and multiple sequence alignment search domains. Our results suggest that when compared with previous anytime algorithms, APTS/ANA⁎APTS/ANA⁎: (1) does not require user-set parameters, (2) finds an initial solution faster, (3) spends less time between solution improvements, (4) decreases the suboptimality bound of the current-best solution more gradually, and (5) converges faster to an optimal solution when reachable.