The central contribution of this dissertation is the proposal of a graded BDI agent model (g-BDI), specifying an architecture capable of representing and reasoning with graded mental attitudes. We consider that making the BDI architecture more flexible will allow us to design and develop agents capable of improved performance in uncertain and dynamic environments, serving other agents (human or not) that may have a set of graded motivations. In the g-BDI model, the agent graded attitudes have an explicit and suitable representation. Belief degrees represent the extent to which the agent believes a formula to be true. Degrees of positive or negative desires allow the agent to set di erent levels of preference or rejection respectively. Intention degrees also give a preference measure but, in this case, modelling the cost/bene t trade o of achieving an agent's goal. Then, agents having di erent kinds of behaviour can be modelled on the basis of the representation and interaction of their graded attitudes. The formalization of the g-BDI agent model is based on Multi-context systems and in order to represent and reason about the beliefs, desires and intentions, we followed a many-valued modal approach. Also, a sound and complete axiomatics for representing each graded attitude is proposed. Besides, in order to cope with the operational semantics aspects of the g-BDI agent model, we rst de ned a Multi-context calculus for Multi-context systems execution and then, using this calculus we give this agent model computational meaning. Furthermore, a software engineering process to develop graded BDI agents in a multiagent scenario is presented. The aim of the proposed methodology is to guide the design of a multiagent system starting from a real world problem. Through the development of a Tourism recommender system, where one of its principal agents is modelled as a g-BDI agent, we show that the model is useful to design and implement concrete agents.
Finally, using the case study we have made some experiments concerning the flexibility and performance of the g-BDI agent model, demonstrating that this agent model is useful to develop agents showing varied and rich behaviours. We also show that the results obtained by these particular recommender agents using graded attitudes improve those achieved by agents using non-graded attitudes