We present a graded BDI agent model (g-BDI) that allow us to define concrete agents having different behaviors, specifying an architecture capable of representing and reasoning with graded mental attitudes. In the g-BDI model, an agent's 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 different levels of preference or rejection respectively. Intention degrees also give a preference measure but, in this case, modelling the cost/benefit trade-off of achieving an agent's goal. Then, agents having different kinds of behaviour can be modelled on the basis of the representation and interaction of their graded beliefs, desires and intentions.

The formalization of the g-BDI agent model is based on multi-context systems (MCS), and in order to represent and reason about the beliefs, desires and intentions, we follow a many-valued modal approach. In order to cope with the operational semantics aspects of the g-BDI agent model, first a calculus for multi-context systems execution has been defined and then, using this calculus, we provide the g-BDI agent model with a 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. Using a tourism reccomender system as a 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 concrete agents showing a variety of 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.