Communication Methodology to Control a Distributed Multi-Agent System

The operation of autonomous multi-agent systems (MAS) under the complex interactions among intelligent agents heavily relies on the consensus protocols to facilitate multi-agent collaboration. In this paper, we novelly study the coordination and control strategy for robust consensus building in a distributed MAS of bayesian social learning agents that make sequential decisions with finite horizon. Unlike the conventional consensus problems in literature, the idiosyncratic information cascading behavior arising from multi-agent social learning poses a new challenge to consensus protocol design. Although cascading can alleviate randomness and enhance consensus, cascading in the early stages of sequential decision making may cause error-prone bifurcation behavior. Therefore, how to properly utilize the advantage of information cascade and meanwhile mitigating its undesirable side effect is critical in the design of consensus protocol to control distributed MAS of social learning agents. Considering communication topology, we uniquely develop a robust cascading strategy for agents adapting to local network neighborhood. Our results show that the proposed strategy not only mitigates early cascading failures but also guarantees multi-agent consensus in various complex network topologies.