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Arguing and negotiating using incomplete negotiators profiles

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Abstract

Computational argumentation has taken a predominant place in the modeling of negotiation dialogues over the last years. A competent agent participating in a negotiation process is expected to decide its next move taking into account an, often incomplete, model of its opponent. This work provides a complete computational account of argumentation-based negotiation under incomplete opponent profiles. After the agent identifies its best option, in any state of a negotiation, it looks for suitable arguments that support this option in the theory of its opponent. As the knowledge on the opponent is uncertain, the challenge is to find arguments that, ideally, support the selected option despite the uncertainty. We present a negotiation framework based on these ideas, along with experimental evidence that highlights the advantages of our approach.

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Notes

  1. If some variable \(x \in V\) does not explicitly belong to any \(X_i\), i.e. \(X_1 \cup \dots \cup X_n \subset V\), then it implicitly means that x can be existentially quantified at the rightmost level.

  2. Since we use the extension-based semantics defined by Dung, we consider binary acceptability statuses for arguments: an argument that is not accepted is rejected.

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Acknowledgements

The authors would like to thank their students Toufik Ider and Mickael Lafages for their excellent work in the implementation of the proposed framework. The authors would like also to thank the reviewers for their very constructive comments that allowed to improve significantly the quality of the paper.

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Authors and Affiliations

  1. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    Yannis Dimopoulos

  2. LIPADE, University of Paris, Paris, France

    Jean-Guy Mailly & Pavlos Moraitis

  3. Argument Theory, Paris, France

    Pavlos Moraitis

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  1. Yannis Dimopoulos
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Correspondence to Yannis Dimopoulos.

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Dimopoulos, Y., Mailly, JG. & Moraitis, P. Arguing and negotiating using incomplete negotiators profiles. Auton Agent Multi-Agent Syst 35, 18 (2021). https://doi.org/10.1007/s10458-021-09493-y

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