Persuasion Dialogues via Restricted Interfaces Using Probabilistic Argumentation

For persuasion dialogues between a software system and user, a user should be able to present arguments. Unfortunately, this would involve natural language processing which is not viable for this task in the short-term. A compromise is to allow the system to present potential counterarguments to the user, and the user expresses his/her degree of belief in each of them. In this paper, we present a protocol for persuasion that supports this type of move, and show how the system can use the epistemic approach to probabilistic argumentation to model the user, and thereby optimize the choice of moves.

[1]  Henry Prakken,et al.  Coherence and Flexibility in Dialogue Games for Argumentation , 2005, J. Log. Comput..

[2]  Peter McBurney,et al.  Opponent Modelling in Persuasion Dialogues , 2013, IJCAI.

[3]  Matthias Thimm,et al.  Strategic Argumentation in Multi-Agent Systems , 2014, KI - Künstliche Intelligenz.

[4]  Phan Minh Dung,et al.  On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and n-Person Games , 1995, Artif. Intell..

[5]  Anthony Hunter.  Opportunities for Argument-Centric Persuasion in Behaviour Change , 2014, JELIA.

[6]  Henry Prakken,et al.  DOI: 10.1017/S000000000000000 Printed in the United Kingdom Formal systems for persuasion dialogue , 2022 .

[7]  Anthony Hunter,et al.  Probabilistic Argumentation with Incomplete Information , 2014, ECAI.

[8]  Nir Oren,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Opponent Models with Uncertainty for Strategic Argumentation , 2022 .

[9]  Francesca Toni,et al.  Assumption-Based Argumentation Dialogues , 2011, IJCAI.

[10]  Anthony Hunter,et al.  Reasons and Options for Updating an Opponent Model in Persuasion Dialogues , 2015, TAFA.

[11]  Martin Caminada,et al.  Grounded Semantics as Persuasion Dialogue , 2012, COMMA.

[12]  Nicolas Maudet,et al.  Empirical Evaluation of Strategies for Multiparty Argumentative Debates , 2014, CLIMA.

[13]  Nicolas Maudet,et al.  Optimization of Probabilistic Argumentation with Markov Decision Models , 2015, IJCAI.

[14]  Francesca Toni,et al.  A general framework for sound assumption-based argumentation dialogues , 2014, Artif. Intell..

[15]  Pietro Baroni,et al.  On Rationality Conditions for Epistemic Probabilities in Abstract Argumentation , 2014, COMMA.

[16]  R. Likert.  “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.

[17]  Anthony Hunter,et al.  A probabilistic approach to modelling uncertain logical arguments , 2013, Int. J. Approx. Reason..

[18]  Anthony Hunter,et al.  Modelling the Persuadee in Asymmetric Argumentation Dialogues for Persuasion , 2015, IJCAI.

[19]  Nir Oren,et al.  Probabilistic Argumentation Frameworks , 2011, TAFA.

[20]  Anthony Hunter.  Probabilistic Strategies in Dialogical Argumentation , 2014, SUM.

[21]  Sarit Kraus,et al.  Providing Arguments in Discussions Based on the Prediction of Human Argumentative Behavior , 2015, AAAI.

[22]  Nava Tintarev,et al.  Formal Arguments, Preferences, and Natural Language Interfaces to Humans: an Empirical Evaluation , 2014, ECAI.

[23]  Matthias Thimm,et al.  A Probabilistic Semantics for abstract Argumentation , 2012, ECAI.

[24]  Phan Minh Dung,et al.  Towards (Probabilistic) Argumentation for Jury-based Dispute Resolution , 2010, COMMA.

[25]  Elizabeth Black,et al.  Automated Planning of Simple Persuasion Dialogues , 2014, CLIMA.

[26]  Anthony Hunter.  Some Foundations for Probabilistic Abstract Argumentation , 2012, COMMA.