Using Ontologies in Hybrid Software Agent Architectures

Having the properties of autonomy, sociability and learning ability, software agents provide a better approach to the increasing complexity of both software problems and solutions and to support the decision making process. An important decision when developing a software agent is the choice of its internal architecture. Several models of deliberative and reactive architectures have already been proposed. However, approaches of hybrid software architectures that combine deliberative and reactive components, with the advantages of both behaviors, are still an open research topic. This work aims at contributing to the development of complex systems through the proposal of ontology-driven hybrid software agent architecture, by exploring automated reasoning and learning techniques. The architecture is composed of a reactive system and a deliberative one. Through a learning mechanism, the set of inference rules in the ontology of the deliberative system are transformed into reactive rules to be used by the reactive system, thus providing a more effective decision.