{"type":"ScholarlyArticle","title":"Attractor-based Closed List Search: Sparsifying the Closed List for Efficient Memory-Constrained Planning","authors":[{"name":"Alvin Zou","slug":"alvin-zou","given":"Alvin","family":"Zou","affiliation":""},{"name":"Muhammad Suhail Saleem","slug":"muhammad-suhail-saleem","given":"Muhammad Suhail","family":"Saleem","affiliation":""},{"name":"Maxim Likhachev","slug":"maxim-likhachev","given":"Maxim","family":"Likhachev","affiliation":""}],"abstract":"Best-first search algorithms such as A* and Weighted A* are widely used tools. However, their high memory requirements often make them impractical for memory-constrained applications, such as on-board planning for interplanetary rovers, drones, and embedded systems. One popular strategy among memory-efficient approaches developed to address this challenge is to eliminate or sparsify the Closed list, a structure that tracks states explored by the search. However, such methods often incur substantial overhead in runtime, requiring recursive searches for solution reconstruction. In this work, we propose Attractor-based Closed List Search (ACLS), a novel framework that sparsely represents the Closed list using a small subset of states, termed attractors. ACLS intelligently identifies attractor states in a way that enables efficient solution reconstruction while preserving theoretical guarantees on the quality of the solution. Furthermore, we also introduce a lazy variant, Lazy-ACLS, which defers the computation of attractor states until necessary, substantially improving planning speed. We demonstrate the efficacy of ACLS used in conjunction with A*, Weighted A*, and Dijkstra’s searches across multiple domains including 2D and 3D navigation, Sliding Tiles, and Towers of Hanoi. Our experimental results demonstrate that ACLS significantly reduces memory usage, maintaining only 9% of the states typically stored in a Closed list, while achieving comparable planning times and outperforming state-of-the-art approaches. Source code can be found at github.com/alvin-ruihua-zou/ACLS.","doi":"10.24963/ijcai.2025/1004","url":"https://ijcai.org/proceedings/2025/1004","pdf":"https://ijcai.org/proceedings/2025/1004.pdf","markdown":"https://ijcai.org/proceedings/2025/1004.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1004","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Search","track":"Main Track","number":1004,"pages":{"first":9031,"last":9038},"keywords":["Search: S: Heuristic search"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}