Anytime Heuristic for Weighted Matching Through Altruism-Inspired Behavior
This provides a scalable solution for large-scale resource allocation problems, such as urban vehicle coordination, though it is incremental as it builds on existing assignment problem methods.
The paper tackles the assignment problem by introducing ALMA, a decentralized anytime heuristic inspired by altruism, which achieves high social welfare and is orders of magnitude faster than centralized optimal algorithms, scaling to hundreds of thousands of agents.
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.