Max-sum with quadtrees for decentralized coordination in continuous domainsMax-sum with quadtrees for decentralized coordination in continuous domains Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2024-12-122022enIn 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.The research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation programme/ERC Grant Agreement n. [833915], project TrafficFluid.http://creativecommons.org/licenses/by/4.0/518-52631st International Joint Conference on Artificial IntelligenceProceedings of the 31st International Joint Conference on Artificial Intelligence Troullinos Dimitrios Τρουλλινος Δημητριος Chalkiadakis Georgios Χαλκιαδακης Γεωργιος Samoladas Vasilis Σαμολαδας Βασιλης Papageorgiou Markos Παπαγεωργιου Μαρκος International Joint Conferences on Artificial Intelligence 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