000268983 001__ 268983
000268983 005__ 20190828105922.0
000268983 037__ $$aCONF
000268983 245__ $$aAnytime Heuristic for Weighted Matching Through Altruism-Inspired Behavior
000268983 260__ $$c2019
000268983 269__ $$a2019
000268983 336__ $$aConference Papers
000268983 520__ $$aWe 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.
000268983 6531_ $$aml-ai
000268983 700__ $$aDanassis, Panayiotis
000268983 700__ $$aFilos-Ratsikas, Aris
000268983 700__ $$aFaltings, Boi
000268983 773__ $$tThe 28th International Joint Conference on Artificial Intelligence (IJCAI '19)
000268983 8560_ $$fsylvie.thomet@epfl.ch
000268983 85641 $$uhttps://arxiv.org/pdf/1902.09359.pdf
000268983 909C0 $$xU10406$$0252184$$pLIA
000268983 909CO $$pconf$$pIC$$ooai:infoscience.epfl.ch:268983
000268983 91899 $$9LIA2019
000268983 973__ $$aEPFL$$rREVIEWED
000268983 980__ $$aCONF