{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T18:25:13Z","timestamp":1763058313743,"version":"build-2065373602"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"01","license":[{"start":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T00:00:00Z","timestamp":1563321600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.aaai.org"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Model-based diagnosis (MBD) is difficult to use in practice because it requires a model of the diagnosed system, which is often very hard to obtain. We explore theoretically how observing the system when it is in a normal state can provide information about the system that is sufficient to learn a partial system model that allows automated diagnosis. We analyze the number of observations needed to learn a model capable of finding faulty components in most cases. Then, we explore how knowing the system topology can help us to learn a useful model from the normal observations for settings in which many of the internal system variables cannot be observed. Unlike other data-driven methods, our learned model is safe, in the sense that subsystems identified as faulty are guaranteed to truly be faulty.<\/jats:p>","DOI":"10.1609\/aaai.v33i01.33013084","type":"journal-article","created":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T07:36:28Z","timestamp":1567928188000},"page":"3084-3091","source":"Crossref","is-referenced-by-count":8,"title":["Safe Partial Diagnosis from Normal Observations"],"prefix":"10.1609","volume":"33","author":[{"given":"Roni","family":"Stern","sequence":"first","affiliation":[]},{"given":"Brendan","family":"Juba","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2019,7,17]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4167\/4045","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/4167\/4045","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T06:42:58Z","timestamp":1667803378000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/4167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,17]]},"references-count":0,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2019,7,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v33i01.33013084","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"type":"electronic","value":"2374-3468"},{"type":"print","value":"2159-5399"}],"subject":[],"published":{"date-parts":[[2019,7,17]]}}}