From Bayesian Decision-Makers to Bayesian Agents

Bayesian approach to decision making is successfully applied in control theory for design of control strategy. However, it is based on on the assumption that a decision-maker is the only active part of the system. Relaxation of this assumption would allow us to build a framework for design of control strategy in multi-agent systems. In Bayesian framework, all information is represented by probability density functions. Therefore, communication and negotiation of Bayesian agents also needs to be facilitated by probabilities. Recent advances in Bayesian theory make formalization these tasks possible. In this paper, we bring the existing theoretic results together and show their relevance for multi-agent systems. The proposed approach is illustrated on the problem of feedback control of an urban traffic network.