Preventing depression relapse by intelligent agent technology. Major focus is to develop a computational dynamic model to understand the behaviour of long-term human social interaction with a machine. The overall aim is the development of a human aware application for a behaviour change therapy in unipolar depression.
When people interact with the environment, errors are inevitable. This is even more apparent when people are working in dynamic industries (e.g. air traffic or naval warfare) as such tasks often entail negative effects on a human’s functional state, leading to a decrease in performance quality. In addition, in these high demanding tasks, some errors can have disastrous consequences. This project explores the influence of different cognitive factors on performance in dynamic tasks, specifically by the design of cognitive models on human functioning. Furthermore, ambient systems that support humans in their interaction with the environment are investigated.
Supervised together with Tibor Bosse.
Crime is a major problem within society. To help prevent crime from happening it is important to understand why crime happens. One approach to get more insight in this phenomenon is by using agent-based simulation. In this project different criminological problems are simulated to comprehend what is happening and how a process can be influenced. The main focus is on three different areas namely the internal functioning of a criminal, the displacement of crime and social learning of criminal behaviour.
Cognitive models for training simulation.