Multiagent systems for cardiac pacing simulation and control
AI Communications - Agents Applied in Health Care
A formal framework for connective stability of highly decentralized cooperative negotiations
Autonomous Agents and Multi-Agent Systems
Local synchronization of a complex network model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
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Market-based optimization is a new optimization method for large decentralized systems where the distributed resource allocation of an economic system is adopted. Market-based algorithms can be interpreted as multi-agent scenarios where producer and consumer agents both compete and cooperate on a market of specified commodities. The market-based approach is applied to the synchronization of a set of local multiple-model systems. The method is extended to the case where each of the subsystems is represented by a Takagi-Sugeno (TS) fuzzy system. Although all local systems are provided with the same control input, the behaviors of the local systems are, in general, different because of different parameters in the subsystems. The task of the market-based optimization is to find an appropriate composition of subsystems so that all local systems exhibit a similar dynamical behavior. Examples show that even systems with potentially unstable local systems can be synchronized if there exists a stable combination of weighted subsystems.