A near-optimal node-to-agent mapping heuristic for GDL-based DCOP algorithms in multi-agent systems

Abstract

© 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. Distributed Constraint Optimization Problems (DCOPs) can be used to model a number of multi-agent coordination problems. The conventional DCOP model assumes that the subproblem that each agent is responsible for (i.e. the mapping of nodes in the constraint graph to agents) is part of the model description. While this assumption is often reasonable, there are many applications where there is some flexibility in making this assignment. In this paper, we focus on this gap and make the following contributions: (1) We formulate this problem as an optimization problem, where the goal is to find an assignment that minimizes the completion time of the DCOP algorithm (e.g. Action-GDL or Max-Sum) that operates on this mapping. (2) We propose a novel heuristic, called MNA, that can be executed in a centralized or decentralized manner. (3) Our empirical evaluation illustrates a substantial reduction in completion time, ranging from 16% to 40%, without affecting the solution quality of the algorithms, compared to the current state of the art. In addition, we observe empirically that the completion time obtained from our approach is near-optimal; it never exceeds more than 10% of what can be achieved from the optimal node-to-agent mapping.