Probability Collectives: A Decentralized, Distributed Optimization for Multi-Agent Systems

Complex systems may have many components that not only interact but also compete with one another to deliver the best they can to reach the desired system objective. As the number of components grows, complexity and communication also grow, making them computationally cumbersome to be treated in a centralized way. It may be better to handle them in a distributed way and decomposed into components/variables that can be seen as a collective of agents or a Multi-Agent System (MAS). The major challenge is to make these agents work in a coordinated way, optimizing their local utilities and contributing towards optimization of the global objective. This paper implements the theory of Collective Intelligence (COIN) using Probability Collectives (PC) in a slightly different way from the original PC approach to achieve the global goal. The approach is demonstrated successfully using Rosenbrock Function in which the variables are seen as agents working independently but collectively towards a global objective.