Hierarchical multiobjective analysis for large-scale systems: reviews and current status
Automatica (Journal of IFAC)
Towards fully probabilistic control design
Automatica (Journal of IFAC)
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Applications of Automatic Control Concepts to Traffic Flow Modeling and Control
Applications of Automatic Control Concepts to Traffic Flow Modeling and Control
Hierarchial generation of pareto optimal solutions in large-scale multiobjective systems
Computers and Operations Research
Hierarchial generation of pareto optimal solutions in large-scale multiobjective systems
Computers and Operations Research
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Optimized Bayesian Dynamic Advising: Theory and Algorithms (Advanced Information and Knowledge Processing)
A Distributed Approach for Coordination of Traffic Signal Agents
Autonomous Agents and Multi-Agent Systems
Control plane algorithms targeting challenging autonomic properties in grey systems
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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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.