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

Roni Stern, Ariel Felner, Jur Van Den Berg, Rami Puzis, Rajat Shah, Ken Goldberg

Artificial Intelligence 214, 1-25, 2014

This paper presents two new search algorithms: Potential Search (PTS) and Anytime Potential Search/Anytime Non-Parametric A⁎(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⁎ 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⁎. We describe experiments that evaluate the new algorithms in the 15-puzzle, KPP-COM, robot motion planning, gridworld navigation, and multiple sequence …