Abstract. This paper presents a dynamic agent model of recurrences of a depression for an individual. Based on several personal characteristics and a representation of events (i.e. life events or daily hassles) the agent model can simulate whether a human agent that recovered from a depression will fall into a relapse or recurrence. A number of well-known relations between events and the course of depression are summarized from the literature and it is shown that the model exhibits those patterns. In addition, the agent model has been mathematically analyzed to find out which stable situations exist. Finally, it is pointed out how this model can be used in depression therapy, supported by a software agent.

One of the challenges for the patients with a history of unipolar depression is to stay healthy throughout their lifetime. In principle, with more prior onset cases, it escalates the risk of the patients to fall into a relapse. In this paper, an ambient agent based model to support patients from relapse is presented. Theories and related works in depression relapse prevention provide a foundation for the formalization of the temporal properties to describe the model. This model was analyzed under several scenarios using simulation and automated verification.

Abstract.Helping someone who is depressed can be very important to the depressed person. A number of supportive family members or friends can often make a big difference. This paper addresses how a social support network can be formed, taking the needs of the support recipient and the possibilities of the potential support providers into account. To do so, dynamic models about the preferences and needs of both support providers and support recipients are exploited. The outcome of this is used as input for a configuration process of a support network. In a case study, it is show how such an intelligently formed network results in a reduced long term stress level.

Abstract