{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:38:05Z","timestamp":1723016285948},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/kr.2020\/14","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:39:16Z","timestamp":1597883956000},"page":"130-140","source":"Crossref","is-referenced-by-count":2,"title":["Explanatory Diagnosis of Discrete-Event Systems with Temporal Information and Smart Knowledge-Compilation"],"prefix":"10.24963","author":[{"given":"Nicola","family":"Bertoglio","sequence":"first","affiliation":[{"name":"University of Brescia"}]},{"given":"Gianfranco","family":"Lamperti","sequence":"additional","affiliation":[{"name":"University of Brescia"}]},{"given":"Marina","family":"Zanella","sequence":"additional","affiliation":[{"name":"University of Brescia"}]},{"given":"Xiangfu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Yantai University"}]}],"member":"10584","event":{"number":"17","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","Association for Logic Programming","Center for Perspicuous Computing","European Association for Artificial Intelligence","Ontopic - The Virtual Knowledge Graph Company"],"acronym":"KR-2020","name":"17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}","start":{"date-parts":[[2020,9,12]]},"theme":"Artificial Intelligence","location":"Rhodes, Greece","end":{"date-parts":[[2020,9,18]]}},"container-title":["Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T16:18:31Z","timestamp":1604593111000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2020\/14"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2020\/14","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}