Analyzing and Improving the Use of the FastMap Embedding in Pathfinding Tasks

Reza Mashayekhi; Dor Atzmon; Nathan R. Sturtevant

doi:10.1609/aaai.v37i10.26469

Analyzing and Improving the Use of the FastMap Embedding in Pathfinding Tasks

Reza Mashayekhi, Dor Atzmon, Nathan R. Sturtevant

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The FastMap algorithm has been proposed as an inexpensive metric embedding which provides admissible distance estimates between all vertices in an embedding. As an embedding, it also supports additional operations such as taking the median location of two vertices, which is important in some problems. This paper studies several aspects of FastMap embeddings, showing the relationship of FastMap to general additive heuristics. As an admissible heuristic, FastMap is not as strong as previous suggested. However, by combining FastMap with the ideas of differential heuristics, we can significantly improve the performance of FastMap heuristics. We show the impact of these ideas in both single-agent pathfinding and the Multi-Agent Meeting problem, where the performance of algorithms using our improved FastMap embedding is improved by up to a factor of two.

Original language	English
Title of host publication	AAAI-23 Technical Tracks 10
Editors	Brian Williams, Yiling Chen, Jennifer Neville
Publisher	AAAI press
Pages	12473-12481
Number of pages	9
ISBN (Electronic)	9781577358800
DOIs	https://doi.org/10.1609/aaai.v37i10.26469
State	Published - 27 Jun 2023
Externally published	Yes
Event	37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States Duration: 7 Feb 2023 → 14 Feb 2023

Publication series

Name	Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Volume	37

Conference

Conference	37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/Territory	United States
City	Washington
Period	7/02/23 → 14/02/23

Bibliographical note

Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Access to Document

10.1609/aaai.v37i10.26469

Cite this

Mashayekhi, R., Atzmon, D., & Sturtevant, N. R. (2023). Analyzing and Improving the Use of the FastMap Embedding in Pathfinding Tasks. In B. Williams, Y. Chen, & J. Neville (Eds.), AAAI-23 Technical Tracks 10 (pp. 12473-12481). (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37). AAAI press. https://doi.org/10.1609/aaai.v37i10.26469

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Mashayekhi, R, Atzmon, D & Sturtevant, NR 2023, Analyzing and Improving the Use of the FastMap Embedding in Pathfinding Tasks. in B Williams, Y Chen & J Neville (eds), AAAI-23 Technical Tracks 10. Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, vol. 37, AAAI press, pp. 12473-12481, 37th AAAI Conference on Artificial Intelligence, AAAI 2023, Washington, United States, 7/02/23. https://doi.org/10.1609/aaai.v37i10.26469

Analyzing and Improving the Use of the FastMap Embedding in Pathfinding Tasks. / Mashayekhi, Reza; Atzmon, Dor; Sturtevant, Nathan R.
AAAI-23 Technical Tracks 10. ed. / Brian Williams; Yiling Chen; Jennifer Neville. AAAI press, 2023. p. 12473-12481 (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Analyzing and Improving the Use of the FastMap Embedding in Pathfinding Tasks

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