Anytime Heuristic for Weighted Matching Through Altruism-Inspired Behavior

Panayiotis Danassis, Aris Filos-Ratsikas, Boi Faltings

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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. We 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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19
PublisherInternational Joint Conferences on Artificial Intelligence Organization
Number of pages8
ISBN (Electronic)978-0-9992411-4-1
Publication statusPublished - 1 Jul 2019
EventInternational Joint Conference in Artificial Intelligence - Macao, China
Duration: 10 Aug 201916 Aug 2019
Conference number: 28th


ConferenceInternational Joint Conference in Artificial Intelligence
Abbreviated titleIJCAI 2019
Internet address


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