{"type":"ScholarlyArticle","title":"Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs","authors":[{"name":"Saaduddin Mahmud","slug":"saaduddin-mahmud","given":"Saaduddin","family":"Mahmud","affiliation":"Department of Computer Science and Engineering , University of Dhaka"},{"name":"Md. Mosaddek Khan","slug":"md-mosaddek-khan","given":"Md. Mosaddek","family":"Khan","affiliation":"Department of Computer Science and Engineering , University of Dhaka"},{"name":"Moumita Choudhury","slug":"moumita-choudhury","given":"Moumita","family":"Choudhury","affiliation":"Department of Computer Science and Engineering , University of Dhaka"},{"name":"Long Tran-Thanh","slug":"long-tran-thanh","given":"Long","family":"Tran-Thanh","affiliation":"School of Electronics and Computer Science , University of Southampton"},{"name":"Nicholas R. Jennings","slug":"nicholas-r-jennings","given":"Nicholas R.","family":"Jennings","affiliation":"Departments of Computing and Electrical and Electronic Engineering , Imperial College London"}],"abstract":"Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multi-agent systems with a set of discrete variables. Later works have extended DCOPs to model problems with a set of continuous variables, named Functional DCOPs (F-DCOPs). In this paper, we combine both of these frameworks into the Mixed Integer Functional DCOP (MIF-DCOP) framework that can deal with problems regardless of their variables' type. We then propose a novel algorithm - Distributed Parallel Simulated Annealing (DPSA), where agents cooperatively learn the optimal parameter configuration for the algorithm while also solving the given problem using the learned knowledge. Finally, we empirically evaluate our approach in DCOP, F-DCOP, and MIF-DCOP settings and show that DPSA produces solutions of significantly better quality than the state-of-the-art non-exact algorithms in their corresponding settings.","doi":"10.24963/ijcai.2020/38","url":"https://ijcai.org/proceedings/2020/38","pdf":"https://ijcai.org/proceedings/2020/0038.pdf","markdown":"https://ijcai.org/proceedings/2020/38.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/38","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":38,"pages":{"first":268,"last":275},"keywords":["Agent-based and Multi-agent Systems: Coordination and Cooperation","Constraints and SAT: Constraint Optimization","Agent-based and Multi-agent Systems: Multi-agent Learning","Constraints and SAT: Distributed Constraints"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24512/file_24512.m3u8","length_sec":268,"provider":"hls"}],"references":[{"position":1,"raw":"[Alrefaei and Andradóttir, 1999] M. 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