TY - GEN
T1 - Analyzing and Improving the Use of the FastMap Embedding in Pathfinding Tasks
AU - Mashayekhi, Reza
AU - Atzmon, Dor
AU - Sturtevant, Nathan R.
N1 - Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2023/6/27
Y1 - 2023/6/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85168250293&partnerID=8YFLogxK
U2 - 10.1609/aaai.v37i10.26469
DO - 10.1609/aaai.v37i10.26469
M3 - Conference contribution
AN - SCOPUS:85168250293
T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
SP - 12473
EP - 12481
BT - AAAI-23 Technical Tracks 10
A2 - Williams, Brian
A2 - Chen, Yiling
A2 - Neville, Jennifer
PB - AAAI press
T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Y2 - 7 February 2023 through 14 February 2023
ER -