Information Systems Frontiers
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Traditionally, communication among agents has been established based on the group commitment to a common ontology which is unfortunately often too strong or unrealistic. In the real world of communicating agents, it is preferred to enable agents to exchange information while they keep their own individual ontology. While this assumption makes agents to represent their knowledge more independently and gives them more flexibility, it adds to the complexity of communication. We believe that agents can overcome this complexity by using their learning capability. The agents can learn any concepts they don't know but want to communicate about from other agents in the multi-agent system they are working in. Our goal in this thesis is to present a general method for agents using ontologies to teach each other concepts to improve their communication and therefore, cooperation abilities. In our method a particular agent which understood a concept only ambiguously intends to learn it by receiving positive and negative examples for that concept from the other agents. Then, utilizing one of the known concept learning methods, the agent learns the concept in question. This learner agent ask the other agents again to get involved in the learning process by taking votes in case of conflicts in the received set of examples. While this method allows agents not to share common ontologies it enables agents to establish common grounds on concepts known only to some of them, if these common grounds are needed during cooperation. In fact, the learned concepts by an agent are compromise among the views of the other agents and in addition, the method improves the autonomy of agents using them significantly.