{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T20:12:50Z","timestamp":1785701570251,"version":"3.56.0"},"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":[[2022,7]]},"abstract":"<jats:p>Abductive explanations take a central place in eXplainable Artificial Intelligence (XAI) by clarifying with few features \n\nthe way data instances are classified. However, instances may have exponentially many minimum-size abductive explanations, and\n\nthis source of complexity holds even for ``intelligible'' classifiers, such as decision trees. When the number of such abductive explanations is huge,\n\ncomputing one of them, only, is often not informative enough. Especially, better explanations than the one\n\nthat is derived may exist. As a way to circumvent this issue, we propose to leverage \n\na model of the explainee, making precise her \/ his preferences about explanations, and to compute only \n\npreferred explanations. In this paper, several models are pointed out and discussed. For each model, we present and\n\nevaluate an algorithm for computing preferred majoritary reasons, where majoritary reasons are specific abductive\n\nexplanations suited to random forests. We show that in practice the preferred majoritary reasons for an instance\n\ncan be far less numerous than its majoritary reasons.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/91","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"643-650","source":"Crossref","is-referenced-by-count":14,"title":["On Preferred Abductive Explanations for Decision Trees and Random Forests"],"prefix":"10.24963","author":[{"given":"Gilles","family":"Audemard","sequence":"first","affiliation":[{"name":"Univ. Artois, CNRS, CRIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steve","family":"Bellart","sequence":"additional","affiliation":[{"name":"Univ. Artois, CNRS, CRIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Louenas","family":"Bounia","sequence":"additional","affiliation":[{"name":"Univ. Artois, CNRS, CRIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frederic","family":"Koriche","sequence":"additional","affiliation":[{"name":"Univ. Artois, CNRS, CRIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Marie","family":"Lagniez","sequence":"additional","affiliation":[{"name":"Univ. Artois, CNRS, CRIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre","family":"Marquis","sequence":"additional","affiliation":[{"name":"Univ. Artois, CNRS, CRIL"},{"name":"Institut Universitaire de France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","theme":"Artificial Intelligence","location":"Vienna, Austria","acronym":"IJCAI-2022","number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2022,7,23]]},"end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:07:38Z","timestamp":1658142458000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/91"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/91","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}