Optimization of Probabilistic Argumentation With Markov Decision Models
| Autor: | Hadoux, Emmanuel, Beynier, Aurélie, Maudet, Nicolas, Weng, Paul, Hunter, Anthony |
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| Přispěvatelé: | Systèmes Multi-Agents (SMA), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), SYSU-CMU Joint Institute of Engineering, SYSU-CMU Shunde International Joint Research Institute, Department of Computer Science, University College of London [London] (UCL), ANR-10-BLAN-0215,LARDONS,Apprentissage et raisonnement pour la décision optimale selon des informations numériques et symboliques(2010), ANR-13-BS02-0004,AMANDE,Argumentation Multilatérale Avancée pour la DElibération(2013), Hadoux, Emmanuel, BLANC - Apprentissage et raisonnement pour la décision optimale selon des informations numériques et symboliques - - LARDONS2010 - ANR-10-BLAN-0215 - BLANC - VALID, Blanc 2013 - Argumentation Multilatérale Avancée pour la DElibération - - AMANDE2013 - ANR-13-BS02-0004 - Blanc 2013 - VALID |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | International Joint Conference on Artificial Intelligence International Joint Conference on Artificial Intelligence, Jul 2015, Buenos Aires, Argentina |
| Popis: | International audience; One prominent way to deal with conflicting viewpoints among agents is to conduct an argumentative debate: by exchanging arguments, agents can seek to persuade each other. In this paper we investigate the problem, for an agent, of optimizing a sequence of moves to be put forward in a debate, against an opponent assumed to behave stochasti-cally, and equipped with an unknown initial belief state. Despite the prohibitive number of states induced by a naive mapping to Markov models, we show that exploiting several features of such interaction settings allows for optimal resolution in practice, in particular: (1) as debates take place in a public space (or common ground), they can readily be modelled as Mixed Observability Markov Decision Processes, (2) as argumentation problems are highly structured, one can design optimization techniques to prune the initial instance. We report on the experimental evaluation of these techniques. |
| Databáze: | OpenAIRE |
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