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

• Published in: Artificial intelligence Vol. 214; pp. 1 - 25
• Main Authors: Stern, Roni, Felner, Ariel, van den Berg, Jur, Puzis, Rami, Shah, Rajat, Goldberg, Ken
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier B.V 01.09.2014; Elsevier
• Subjects: Algorithms; Anytime algorithms; Applied sciences; Biological and medical sciences; Biotechnology; Computer science; control theory; systems; Control theory. Systems; Data processing. List processing. Character string processing; Derivation; Exact sciences and technology; Expert systems; Fundamental and applied biological sciences. Psychology; Genetic engineering; Genetic technics; Heuristic search; Memory organisation. Data processing; Methods. Procedures. Technologies; Navigation; Repairing; Robotics; Robots; Search algorithms; Searching; Software; Synthetic digonucleotides and genes. Sequencing; Anytime algorithms; Robotics; Heuristic search; Search problem; Suboptimal control; Navigation; Sequence alignment; Path planning; Search algorithm; Function evaluation; Anytime algorithm; Optimal solution; Heuristic method; Repair; Motion control; Robot
• ISSN: 0004-3702; 1872-7921
• DOI: 10.1016/j.artint.2014.05.002

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.