Rhodes, Greece. September 12-18, 2020.
Copyright © 2020 International Joint Conferences on Artificial Intelligence Organization
Model-based diagnosis is typically set-oriented. In static systems, such as combinational circuits, a candidate (or diagnosis) is a set of faulty components that explains a set of observations. In discrete-event systems (DESs), a candidate is a set of faulty events occurring in a sequence of state changes that conforms with a sequence of observations. Invariably, a candidate is a set. This set-oriented perspective makes diagnosis of DESs narrow in explainability, owing to the lack of any temporal knowledge relevant to the faults within a candidate, along with the inability to discriminate between single and multiple occurrences of the same fault. Embedding temporal knowledge in a candidate, such as the relative temporal ordering of faults and the multiplicity of the same fault, may be essential for critical decision making. To favor explainability, the notions of temporal fault, explanation, and explainer are introduced in diagnosis of DESs. The explanation engine reacts to a given sequence of observations by generating and refining in real-time a sequence of regular expressions, where the language of each expression is a set of temporal faults. Moreover, to avoid total knowledge compilation, the explainer can be generated incrementally either offline, based on meaningful behavioral scenarios, or online, when being operated in solving specific diagnosis problems.