TY - JOUR
T1 - Potential-based bounded-cost search and Anytime Non-Parametric A *
AU - Stern, Roni
AU - Felner, Ariel
AU - Van Den Berg, Jur
AU - Puzis, Rami
AU - Shah, Rajat
AU - Goldberg, Ken
N1 - Funding Information:
This research was supported by the Israel Science Foundation (ISF) under grant number 417/13 to Ariel Felner. This work was supported in part (for Goldberg and van den Berg) by the US National Science Foundation under Award IIS-1227536 . We thank Maxim Likhachev for making his implementation of ARA ⁎ available to us, as well as publishing the code for ANA ⁎ . Our implementation of ANA ⁎ is freely available in his Search-based Planning Library (SBPL) at: http://www.sbpl.net .
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
KW - Anytime algorithms
KW - Heuristic search
KW - Robotics
UR - https://www.scopus.com/pages/publications/84901418825
U2 - 10.1016/j.artint.2014.05.002
DO - 10.1016/j.artint.2014.05.002
M3 - Article
AN - SCOPUS:84901418825
SN - 0004-3702
VL - 214
SP - 1
EP - 25
JO - Artificial Intelligence
JF - Artificial Intelligence
ER -