{"type":"ScholarlyArticle","title":"Anytime Heuristic for Weighted Matching Through Altruism-Inspired Behavior","authors":[{"name":"Panayiotis Danassis","slug":"panayiotis-danassis","given":"Panayiotis","family":"Danassis","affiliation":"Artificial Intelligence Laboratory , École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland"},{"name":"Aris Filos-Ratsikas","slug":"aris-filos-ratsikas","given":"Aris","family":"Filos-Ratsikas","affiliation":"Artificial Intelligence Laboratory , École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland"},{"name":"Boi Faltings","slug":"boi-faltings","given":"Boi","family":"Faltings","affiliation":"Artificial Intelligence Laboratory , École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland"}],"abstract":"We present a novel anytime heuristic (ALMA), inspired by the human principle of altruism, for solving the assignment problem. ALMA is decentralized, completely uncoupled, and requires no communication between the participants. We prove an upper bound on the convergence speed that is polynomial in the desired number of resources and competing agents per resource; crucially, in the realistic case where the aforementioned quantities are bounded independently of the total number of agents/resources, the convergence time remains constant as the total problem size increases.\r\nWe have evaluated ALMA under three test cases: (i) an anti-coordination scenario where agents with similar preferences compete over the same set of actions, (ii) a resource allocation scenario in an urban environment, under a constant-time constraint, and finally, (iii) an on-line matching scenario using real passenger-taxi data. In all of the cases, ALMA was able to reach high social welfare, while being orders of magnitude faster than the centralized, optimal algorithm. The latter allows our algorithm to scale to realistic scenarios with hundreds of thousands of agents, e.g., vehicle coordination in urban environments.","doi":"10.24963/ijcai.2019/31","url":"https://ijcai.org/proceedings/2019/31","pdf":"https://ijcai.org/proceedings/2019/0031.pdf","markdown":"https://ijcai.org/proceedings/2019/31.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/31","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":31,"pages":{"first":215,"last":222},"keywords":["Agent-based and Multi-agent Systems: Multi-agent Learning","Agent-based and Multi-agent Systems: Resource Allocation","Agent-based and Multi-agent Systems: Coordination and Cooperation"],"videos":[],"references":[{"position":1,"raw":"[Agatz et al., 2011] Niels A.H. 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