A Spectrum of Symbolic On-line Diagnosis Approaches

This paper deals with the monitoring and diagnosis of large discrete-event systems. The problem is to determine, online, all faults and states that explain the flow of observations. Model-based diagnosis approaches that first compile the diagnosis information off-line suffer from space explosion, and those that operate on-line without any prior compilation have poor time performance. Our contribution is a broader spectrum of approaches that suits applications with diverse time and space requirements. Approaches on this spectrum differ in the amount of reasoning and compilation performed off-line and therefore in the way they resolve the tradeoff between the space occupied by the compiled information and the time taken to produce a diagnosis. We tackle the space and time complexity of diagnosis by encoding all approaches in a symbolic framework based on binary decision diagrams. This allows for the compact representation of the compiled diagnosis information, and for its handling acr...

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: Anika Schumann, Yannick Pencolé, Sylvie Thiébaux
• Keywords: AAAI 2007, Diagnosis Information, Intelligent Agents, Model-based Diagnosis, Space