Given a set of agents with valid previous knowledge bases obtained by a generator of beliefs, we wish to know how new knowledge affects each agent. To model the new knowledge, boolean logic is used, expressed by 2CNF clauses, to reduce the complexity. Upon recieving new knowledge, one or more agents may find it inconsistent with their previous knowledge base, so a mechanism is applied which removes knowledge by using a contraction operation,described by the AGM model in order to ensure consistency of the knowledge base. The goal is to determinate if that contradicting knowledge significantly affects the set of beliefs of each agent.

Furthermore, a problem is modeled in which, given a set of agents (production operators) and their knowledge base (skills in the industry), clauses represent new activities are added when a new process is required and the model determines which is the most suitable agent to perform the activities, through from an evaluation mechanism of the inconsistencies that was generated.

We have developed software which allows to support make decisions over the more capable workers to develop an activity based on their previous skills (knowledge).