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.

提出了一种新颖的任意时刻启发式算法（ALMA），用于解决分配问题，具有分散、不耦合和无通信等特点，并证明了其在资源和竞争代理人数目有多项式上限时的收敛速度，并在三种测试情况下对其进行了评估，即反协调情形，在城市环境中的资源分配情形，在线匹配情境，表明ALMA不仅能够获得高社会福利，而且比中心化最优算法快几个数量级，使算法能够扩展到有数十万个代理的现实情况，例如城市环境中的车辆协调。

通过自利行为启发的加权匹配启发式方法