Optimization of Probabilistic Argumentation with Markov Decision Models

  title={Optimization of Probabilistic Argumentation with Markov Decision Models},
  author={Emmanuel Hadoux and Aur{\'e}lie Beynier and Nicolas Maudet and Paul Weng and Anthony Hunter},
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 stochastically, 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… CONTINUE READING
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  • Matthias Thimm. Strategic argumentation in multi-agent syst Intelligenz, Special Issue on Multi-Agent Decision Making
  • June
  • 2014

Hunter . Probabilistic strategies in dialogical argumentation

  • Dionysios Kontarinis, Elise Bon-zon, Nicolas Maudet
  • International Conference on Scalable Uncertainty…
  • 2014
1 Excerpt

In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI’13)

  • Tjitze Rienstra, Matthias Thimm, Nir Oren. Opponent models with uncertainty for strateg argumentation
  • August
  • 2013

pages 15–20

  • Elizabeth Black, Anthony Hunter. Executable logic for dialogical argumentation
  • Frontiers in Artificial Intelligence and…
  • 2012
1 Excerpt

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