{"type":"ScholarlyArticle","title":"Max-Sum with Quadtrees for Decentralized Coordination in Continuous Domains","authors":[{"name":"Dimitrios Troullinos","slug":"dimitrios-troullinos","given":"Dimitrios","family":"Troullinos","affiliation":"Technical University of Crete , Chania , Greece"},{"name":"Georgios Chalkiadakis","slug":"georgios-chalkiadakis","given":"Georgios","family":"Chalkiadakis","affiliation":"Technical University of Crete , Chania , Greece"},{"name":"Vasilis Samoladas","slug":"vasilis-samoladas","given":"Vasilis","family":"Samoladas","affiliation":"Technical University of Crete , Chania , Greece"},{"name":"Markos Papageorgiou","slug":"markos-papageorgiou","given":"Markos","family":"Papageorgiou","affiliation":"Technical University of Crete , Chania , Greece"}],"abstract":"In this paper we put forward a novel extension of the classic Max-Sum algorithm to the framework of Continuous Distributed Constrained Optimization Problems (Continuous DCOPs), by utilizing a popular geometric algorithm, namely Quadtrees. In its standard form, Max-Sum can only solve Continuous DCOPs with an a priori discretization procedure. Existing Max-Sum extensions to continuous multiagent coordination domains require additional assumptions regarding the form of the factors, such as access to the gradient, or the ability to model them as continuous piecewise linear functions. Our proposed approach has no such requirements: we model the exchanged messages with Quadtrees, and, as such, the discretization procedure is dynamic and embedded in the internal Max-Sum operations (addition and marginal maximization). We apply Max-Sum with Quadtrees to lane-free autonomous driving. Our experimental evaluation showcases the effectiveness of our approach in this challenging coordination domain.","doi":"10.24963/ijcai.2022/74","url":"https://ijcai.org/proceedings/2022/74","pdf":"https://ijcai.org/proceedings/2022/0074.pdf","markdown":"https://ijcai.org/proceedings/2022/74.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/74","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":74,"pages":{"first":518,"last":526},"keywords":["Agent-based and Multi-agent Systems: Coordination and Cooperation","Multidisciplinary Topics and Applications: Other","Multidisciplinary Topics and Applications: Transportation","Planning and Scheduling: Distributed; Multi-agent Planning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986718","length_sec":89,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984861","length_sec":358,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Choudhury et al., 2020] M. 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