Local Coordination in Online Distributed Constraint Optimization Problems

Abstract

In cooperative multi-agent systems, group performance often depends more on the interactions between team members, rather than on the performance of any individual agent. Hence, coordination among agents is essential to optimize the group strategy. One solution which is common in the literature is to let the agents learn in a joint action space. Joint Action Learning (JAL) enables agents to explicitly take into account the actions of other agents, but has the significant drawback that the action space in which the agents must learn scales exponentially in the number of agents. Local coordination is a way for a team to coordinate while keeping communication and computational complexity low. It allows the exploitation of a specific dependency structure underlying the problem, such as tight couplings between specific agents. In this paper we investigate a novel approach to local coordination, in which agents learn this dependency structure, resulting in coordination which is beneficial to the group performance. We evaluate our approach in the context of online distributed constraint optimization problems.