Model-based diagnosis and planning provide means to handle errors and unanticipated situations in today's increasingly complex embedded systems. In my thesis I aim to address two problems that arise in this context: 1) a tighter integration of diagnosis and planning, which can be achieved using constraint optimization problems (COP) as common representation for both of them and 2) a more flexible belief state approximation which can be developed based on this representation using adaptive abstraction. Within this context, based on the existing COP algorithm Mini-Bucket Elimination I have implemented an algorithm which incorporates an adaptive abstraction method using domain abstraction.
Subjects: 1.5 Diagnosis; 1.11 Planning
Submitted: Apr 8, 2008