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Computer Science > Multiagent Systems

arXiv:2310.02346v1 (cs)
[Submitted on 3 Oct 2023]

Title:Evaluating Heuristic Search Algorithms in Pathfinding: A Comprehensive Study on Performance Metrics and Domain Parameters

Authors:Aya Kherrour (University of Trento), Marco Robol (University of Trento), Marco Roveri (University of Trento), Paolo Giorgini (University of Trento)
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Abstract:The paper presents a comprehensive performance evaluation of some heuristic search algorithms in the context of autonomous systems and robotics. The objective of the study is to evaluate and compare the performance of different search algorithms in different problem settings on the pathfinding domain. Experiments give us insight into the behavior of the evaluated heuristic search algorithms, over the variation of different parameters: domain size, obstacle density, and distance between the start and the goal states. Results are then used to design a selection algorithm that, on the basis of problem characteristics, suggests the best search algorithm to use.
Comments: In Proceedings AREA 2023, arXiv:2310.00333
Subjects: Multiagent Systems (cs.MA); Robotics (cs.RO)
Cite as: arXiv:2310.02346 [cs.MA]
  (or arXiv:2310.02346v1 [cs.MA] for this version)
  https://doi.org/10.48550/arXiv.2310.02346
Journal reference: EPTCS 391, 2023, pp. 102-112
Related DOI: https://doi.org/10.4204/EPTCS.391.12

Submission history

From: EPTCS [view email] [via EPTCS proxy]
[v1] Tue, 3 Oct 2023 18:25:25 UTC (878 KB)
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