Anchor Search: A Unified Framework for Suboptimal Bidirectional Search

Sepehr Lavasani, Lior Siag, Shahaf S. Shperberg, Ariel Felner, Nathan R. Sturtevant

Research output: Contribution to journalConference articlepeer-review

Abstract

In recent years the understanding of optimal bidirectional heuristic search (BiHS) has progressed significantly. Yet, BiHS is relatively unexplored in unbounded suboptimal search. Front-to-end (F2E) and front-to-front (F2F) bidirectional search have been used in optimal algorithms, but adapting them for unbounded suboptimal search remains an open challenge. We introduce a framework for suboptimal BiHS, called anchor search, and use it to derive a parameterized family of algorithms. Because our new algorithms need F2F heuristic evaluations, we propose using pattern databases (PDBs) as differential heuristics (DHs) to construct F2F heuristics. Our experiments evaluate three anchor search instances across diverse domains, outperforming existing methods, particularly as the search scales.

Original languageEnglish
Pages (from-to)27045-27053
Number of pages9
JournalProceedings of the AAAI Conference on Artificial Intelligence
Volume39
Issue number25
DOIs
StatePublished - 11 Apr 2025
Event39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025 - Philadelphia, United States
Duration: 25 Feb 20254 Mar 2025

ASJC Scopus subject areas

  • Artificial Intelligence

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