Optimization of Probabilistic Argumentation with Markov Decision Models

@inproceedings{Hadoux2015OptimizationOP,
  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},
  booktitle={IJCAI},
  year={2015}
}
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
Highly Cited
This paper has 38 citations. REVIEW CITATIONS
23 Citations
22 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 22 references

28(3):159–168

  • 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

Similar Papers

Loading similar papers…