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Controlling heat engines imposes to deal with high dynamics, non-linearity and multiple interdependencies.  A way handle these difficulties is enable the controller to learn how the engine behaves, hence avoiding the costly use of an explicit model of the process. Adaptive Multi-Agent Systems (AMAS) are able to learn and  to adapt themselves to their environment thanks to the cooperative self-organization of their agents. A change in the organization of the agents results in a change of the emergent function. Thus we assume that AMAS are a good alternative for complex systems control, reuniting learning, adaptivity, robustness and genericity. In this paper, we present an AMAS for the control of heat engines and show several results

Self-organizing Agents for an Adaptive Control of Heat Engines