Multi-Variable Agents Decomposition for DCOPs to Exploit Multi-Level Parallelism

Abstract

Current DCOP algorithms suffer from a major limiting assumption---each agent can handle only a single variable of the problem---which limits their scalability. This paper proposes a novel Multi-Variable Agent (MVA) DCOP decomposition, which: (i) Exploits co-locality of an agent's variables, allowing us to adopt efficient centralized techniques; (ii) Enables the use of hierarchical parallel models, such us those based on GPGPUs; and (iii) Empirically reduces the amount of communication required in several classes of DCOP algorithms. Experimental results show that our MVA decomposition outperforms non-decomposed DCOP algorithms, in terms of network load and scalability.