Research

My previous research focused on problem solving processes that involve abductive inference (e.g., medical diagnosis). Abductive inference is realized through  a knowledge-intensive case-based reasoning (CBR) model. This model relies on the utilization of a combination of case-specific(i.e., episodic) knowledge, and general domain (i.e., semantic) knowledge.  Context-sensitivity has been a special focus as an element affecting, especially,  the hypothesis generation stage of the abductive reasoning. Currently we are adressing the issue of flexible, context-sensitive weighting of features represented in case-specific knowledge.

Another line of research has been cooperative multi-agent systems, with a special emphasis on task decomposition and allocation of subtask to agents, that is team formation.

My most recent research interest is understanding the connection between top-down and bottom up-processing of information. For this purpose, we are trying to combine ideas, findings, and hypotheses, from  cognitive psychology, ethology and neuroscience. Two of our current focuses are action selection, and imitative learning. We are using robot simulations for modelling motor/robot control and learning involved in imitative behaviour.  

Teaching

2005-2006:            

-TDT4280 Distributed Artificial Intelligence and Intelligent Agents
- IT3706   Knowledge Representation and Modelling
- T8001      Context-sensitive applications

Publications

PhD students