{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:47:29Z","timestamp":1780609649887,"version":"3.54.1"},"reference-count":113,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Argument &amp; Computation"],"published-print":{"date-parts":[[2023,6,7]]},"abstract":"<jats:p>Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is \u201cgood\u201d in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants that compares an automated persuasion system based on this technology with a baseline system that does not take the beliefs and concerns into account in its strategy.<\/jats:p>","DOI":"10.3233\/aac-210005","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T11:46:57Z","timestamp":1671796017000},"page":"109-161","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Strategic argumentation dialogues for persuasion: Framework and experiments based on modelling the beliefs and concerns of the persuadee"],"prefix":"10.1177","volume":"14","author":[{"given":"Emmanuel","family":"Hadoux","sequence":"first","affiliation":[{"name":"Department of Computer Science, University College London, UK"},{"name":"Scribe Labs, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"Hunter","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University College London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sylwia","family":"Polberg","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, Cardiff University, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"ref001","doi-asserted-by":"crossref","unstructured":"L.\u00a0Amgoud and J.\u00a0Ben-Naim, Ranking-based semantics for argumentation frameworks, in:\n                      Proceedings of SUM\u201913\n                      , W.\u00a0Liu, V.S.\u00a0Subrahmanian and J.\u00a0Wijsen, eds, LNCS, Vol.\u00a08078, Springer, 2013, pp.\u00a0134\u2013147.","DOI":"10.1007\/978-3-642-40381-1_11"},{"key":"ref002","unstructured":"K.\u00a0Atkinson, P.\u00a0Bench-Capon and T.\u00a0Bench-Capon, Efficiency in persuasion dialogues, in:\n                      Joaquim Filipe and Ana\n                      , L.N.\u00a0Fred, ed. 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