Communication-Sensitive Pseudo-Tree Heuristics for DCOP Algorithms

Abstract

Distributed Constraint Optimization Problem (DCOP) is a powerful paradigm to model multi-agent systems through enabling multiple agents to coordinate with each other to solve a problem. These agents are often assumed to be cooperative, that is, they communicate with other agents in order to optimize a global objective. However, the communication times between all pairs of agents are assumed to be identical in the evaluation of most DCOP algorithms. This assumption is impractical in almost all real-world applications. In this paper, we study the impact of empirically evaluating a DCOP algorithm under the assumption that communication times between pairs of agents can vary. In addition, we evaluate a DCOP algorithm using ns-2, a discrete-event simulator that is widely used in the computer networking community, to simulate the communication times, as opposed to the standard DCOP simulators that are used to evaluate DCOP algorithms in the AI community. Furthermore, we propose heuristics that exploit the non-uniform communication times to speed up DCOP algorithms that operate on pseudo-trees. Our empirical results demonstrate that the proposed heuristics improve the runtime of those algorithms up to 20%. These heuristics are evaluated on different benchmarks such as scale-free graphs, random graphs, and an instance of the smart grid, Customer-Driven Microgrid (CDMG) application.