Minimizing State Exploration While Searching Graphs with Unknown Obstacles

Daniel Koyfman, Dor Atzmon, Shahaf S. Shperberg, Ariel Felner

Research output: Contribution to journalConference articlepeer-review

Abstract

We address the challenge of finding a shortest path in a graph with unknown obstacles where the exploration cost to detect whether a state is free or blocked is very high (e.g., due to sensor activation for obstacle detection). The main objective is to solve the problem while minimizing the number of explorations. To achieve this, we propose MXA, a novel heuristic search algorithm based on A. The key innovation in MXA lies in modifying the heuristic calculation to avoid obstacles that have already been revealed. Furthermore, this paper makes a noteworthy contribution by introducing the concept of a dynamic heuristic. In contrast to the conventional static heuristic, a dynamic heuristic leverages information that emerges during the search process and adapts its estimations accordingly. By employing a dynamic heuristic, we suggest enhancements to MXA based on real-time information obtained from both the open and closed lists. We demonstrate empirically that MXA finds the shortest path while significantly reducing the number of explored states compared to traditional A. The code is available at https://github.com/bernuly1/MXA-Star.

Original languageEnglish
Pages (from-to)1038-1046
Number of pages9
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2024-May
StatePublished - 1 Jan 2024
Event23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024 - Auckland, New Zealand
Duration: 6 May 202410 May 2024

Keywords

  • A*
  • Minimizing Exploration
  • Unknown Obstacles

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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