{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T13:27:56Z","timestamp":1777728476541,"version":"3.51.4"},"reference-count":0,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Intelligenza Artificiale: The international journal of the AIxIA"],"published-print":{"date-parts":[[2014,2]]},"abstract":"<jats:p>Repairing a plan executed in a partially observable environment is a challenging task; especially when the plan to be repaired is part of a Multiagent Plan (MAP), and hence the synchronization among the agents further constrains the repair process. The paper formalizes a local plan repair strategy, where each agent in a MAP is responsible for controlling (monitoring and diagnosing) the actions it executes, and for autonomously repairing its own plan whenever an action failure is detected. The paper also describes how to mitigate the impact of an action failure on the plans of other agents when the local recovery strategy fails.<\/jats:p>","DOI":"10.3233\/ia-140062","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T12:16:53Z","timestamp":1575289013000},"page":"71-85","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Plan repair driven by model-based agent diagnosis"],"prefix":"10.1177","volume":"8","author":[{"given":"Roberto","family":"Micalizio","sequence":"first","affiliation":[{"name":"Dipartimento di Informatica, Universit\u00e0 di Torino, Torino, Italy"}]}],"member":"179","published-online":{"date-parts":[[2014,1]]},"container-title":["Intelligenza Artificiale: The international journal of the AIxIA"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IA-140062","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IA-140062","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T10:52:13Z","timestamp":1777459933000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IA-140062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,1]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2014,2]]}},"alternative-id":["10.3233\/IA-140062"],"URL":"https:\/\/doi.org\/10.3233\/ia-140062","relation":{},"ISSN":["1724-8035","2211-0097"],"issn-type":[{"value":"1724-8035","type":"print"},{"value":"2211-0097","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,1]]}}}