Minimizing State Exploration While Searching Graphs with Unknown Obstacles (Extended Abstract)

Daniel Koyfman, Shahaf S. Shperberg, Dor Atzmon, 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)273-274
Number of pages2
JournalThe International Symposium on Combinatorial Search
Volume17
Issue number1
DOIs
StatePublished - 2024
Event17th International Symposium on Combinatorial Search, SoCS 2024 - Kananaskis, Canada
Duration: 6 Jun 20248 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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