Abstract-The article presents el modelo e implementaciçon de a multiagent system fuzzy (SMAF), which allows the users to input fuzzy incidents y determinar las relaciones de descendientes y ascendientes entre ellos. SMAF acepta los incidents fuzzy with todas las imprecisiones de sus severity degree tal como lo expresan los usuarios. Tales impresiciones are translated into computational language and are recorded in a knowledge base. En primera instancia SMAF busca en su base de conocimientos entre los incidentes relacionados, las posibles soluciones del incidente planteado y se las sugiere al usuario. If in the first try there is not a solution, the search is expanded to the whole data base, analyzing and suggesting as possible solutions the ones of the incidents which have proximity or inclusion relationship with the original. Then the human experts must accept or reject the solutions suggested by the software agents. If there is no solution found, or if they are not confirmed, SMAF transfers the incident to an expert, human or software.

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The transformation of the tacit knowledge of a company’s human resources into permanent organizational capital in spite of possible staff turnover is of business interest. This research focuses on the management of tacit knowledge to resolve labor incidents and reduce resolution times. We present the GESTAC model, a name derived from the first syllables of the Spanish words “gestión” (management) and “tácito” (tacit), which identifies, locates and rates people in the business domain capable of resolving a labor incident logged by a user employed by the company. In order to achieve its objective, the GESTAC model follows the tacit knowledge management paradigm, according to which tacit knowledge that could eventually resolve the logged incidents is identified, captured and stored in a permanent database, and then evaluated and disseminated to the people who have need of the knowledge. This could lead to the knowledge source being automatically rerated, and the entire process restarted. The aim is to contribute to the state of the art, showing that by applying tacit knowledge management to a specific domain the GESTAC model is able to reduce incident resolution times with respect to traditional systems. The model was prototyped (GESTAC_APP) using the multiagent systems paradigm.