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This paper aims to present a learning model for coordination schemes in Multi-Agent Systems (MAS) based on Cultural Algorithms (CA). In this model, the individuals (one of the CA components) are the different conversations that may occur in any multi-agent systems, and the coordination scheme learned is at the level of the way to perform the communication protocols into the conversation. A conversation can has sub-conversations, and the sub-conversations and/or conversations are identified with a particular type of conversation associated with a certain interaction patterns. The interaction patterns use the coordination mechanisms existing in the literature. In order to simulate the proposed learning model, we develop a computational tool called CLEMAS, which has been used to apply the model to a case of study in industrial automation, related to a Faults Management System based on Agents.

The good performance of a set of computer systems based on agents depends on the coherence degree and coordination between their activities. The study of coordination problem is an important topic for designers and researchers in the multi-agents systems field. There are several coordination mechanisms in the current literature, being the auction and the contract net the most popular ones. These mechanisms allow the agents to allocate resources and tasks to achieve their objectives. This paper aims to present formal models of the auction and the contract net as coordination mechanisms in multi-agents systems based on FIPA (Foundation for Intelligent Physical Agents) Protocols. Mathematical equations describe the different parameters characterizing the auction and the contract net mechanisms; they allow define a generic structure of each mechanism and groups of agents can create several instances of them to coordinate their needs.