# Strategic Argumentation Dialogues for Persuasion: Framework and Experiments Based on Modelling the Beliefs and Concerns of the Persuadee

arXiv: https://arxiv.org/abs/2101.11870 · HTML: https://mlsanity.com/paper/2101.11870/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 114 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions](https://mlsanity.com/paper/1602.01103/) (2016)
- [Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion](https://mlsanity.com/paper/1708.09085/) (2017)
- [A Comparative Study of Ranking-based Semantics for Abstract Argumentation](https://mlsanity.com/paper/1602.01059/) (2016)
- [Epistemic Graphs for Representing and Reasoning with Positive and Negative Influences of Arguments](https://mlsanity.com/paper/1802.07489/) (2018)
- [Empirical Evaluation of Abstract Argumentation: Supporting the Need for Bipolar and Probabilistic Approaches](https://mlsanity.com/paper/1707.09324/) (2017)
- [Argument Harvesting Using Chatbots](https://mlsanity.com/paper/1805.04253/) (2018)
- [Optimization of Information-Seeking Dialogue Strategy for Argumentation-Based Dialogue System](https://mlsanity.com/paper/1811.10728/) (2018)
- [Impact of Argument Type and Concerns in Argumentation with a Chatbot](https://mlsanity.com/paper/1905.00646/) (2019)
- [Domain Modelling in Computational Persuasion for Behaviour Change in Healthcare](https://mlsanity.com/paper/1802.10054/) (2018)
- [A Polynomial-time Fragment of Epistemic Probabilistic Argumentation (Technical Report)](https://mlsanity.com/paper/1811.12083/) (2018)
- [Polynomial-time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation (Technical Report)](https://mlsanity.com/paper/1906.05066/) (2019)

## Built on by (most cited first, 7 of 7)

- [Computational Argumentation-based Chatbots: a Survey](https://mlsanity.com/paper/2401.03454/) (2024)
- [Persuasion Games using Large Language Models](https://mlsanity.com/paper/2408.15879/) (2024)
- [A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs](https://mlsanity.com/paper/2410.22209/) (2024)
- [Fostering User Engagement in the Critical Reflection of Arguments](https://mlsanity.com/paper/2308.09061/) (2023)
- [Reflecti-Mate: A Conversational Agent for Adaptive Decision-Making Support Through System 1 and System 2 Thinking](https://mlsanity.com/paper/2605.22509/) (2026)
- [A Chatbot for Asylum-Seeking Migrants in Europe](https://mlsanity.com/paper/2407.09197/) (2024)
- [Splitting Assumption-Based Argumentation Frameworks](https://mlsanity.com/paper/2604.27964/) (2026)

## Abstract (arXiv)

Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is good in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants showing that our automated persuasion system based on this technology is superior to a baseline system that does not take the beliefs and concerns into account in its strategy.