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

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

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

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 alignment search domains. Our results suggest that when compared with previous anytime algorithms, 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.

Original languageEnglish
Pages (from-to)1-25
Number of pages25
JournalArtificial Intelligence
Volume214
DOIs
StatePublished - 1 Jan 2014

Keywords

  • Anytime algorithms
  • Heuristic search
  • Robotics

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