A theory of diagnosis from first principles
Artificial Intelligence
Information Processing Letters
Remote Agent: to boldly go where no AI system has gone before
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Model-based diagnosis of hardware designs
Artificial Intelligence
Real-time localization and elevation mapping within urban search and rescue scenarios: Field Reports
Journal of Field Robotics
Journal of Field Robotics - Special Issue on Teamwork in Field Robotics
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Navigation software of autonomous mobile robots comprises a number of software modules that typically interact in a very complex way. Their proper interaction and the robustness of each single module strongly influence the safety during navigation in the field. Particularly in unstructured environments, unforeseen situations are likely to occur causing erroneous behaviors of the robot. The proper handling of such situations requires an understanding of cause and effect within the complex interactions of the system. In this paper we present a method for the automatic modeling of navigation software components and their interactions by observing their communication patterns. The learned model is used online for model-based reasoning (MBR) in order to increase system robustness during runtime. We evaluated the approach on three different robot systems whose software components are communicating via the widely used IPC (Inter Process Communication) architecture. Our results demonstrate the systems capability of automatic system learning and diagnosis without a priori knowledge.