Probability Collectives for decentralized, distributed optimization: A Collective Intelligence Approach

The growing number of components and communication in complex systems requires them to be treated as a collective of subsystems/agents having distributed and decentralized control. The major challenge in such approach is the coordination among agents optimizing their local goals and contributing towards optimization of the global objective. This paper implements the theory of collective intelligence (COIN) using probability collectives (PC) approach to achieve the global goal. This approach works on probability distribution, directly incorporating uncertainty and has deep connections to game theory, statistical physics and optimization. In this approach, the agents select actions over a particular range and receive some rewards on the basis of the overall system objective achieved because of those actions. The approach is illustrated using the problem of segmented beam minimizing total volume. Each segment is considered as an agent competing with one another to achieve the total minimum volume. The implementation produced encouraging results.