Explainable and Computationally Efficient Decision Making with Quantitative Abstract Argumentation Frameworks

Abstract argumentation graphs model arguments and relationships between them. Quantitative bipolar graphs are one popular instance with many recent applications. In this setting, relationships are usually attacks and supports and the credibility of arguments is evaluated by numerical values like probabilities or more general strength values. This structure allows to model decision problems very naturally. A decision can be based on pro and contra arguments, which, in turn, may attack or support each other. In many cases, the final decision can be explained very intuitively from the argumentation graph by going backwards through attackers and supporters and their final strength values.

In this tutorial, we will focus on two approaches to quantitative abstract argumentation. Epistemic probabilistic argumentation as proposed by the workgroups of Hunter and Thimm [1] and gradual argumentation as proposed by the workgroups of Amgoud, Baroni and Toni [2, 3]. In both frameworks, many interesting reasoning problems can be solved in polynomial time and the results are easily interpretable and explainable from the graph structure. We will discuss the basic formal models, look at some modelling examples and recent applications [4, 5, 6].

The tutorial will be mostly self-contained and no prior knowledge will be required. However, very basic knowledge about probability theory may be helpful.

1. Introduction and Overview
2. Probabilistic Epistemic Argumentation
   1. Model
   2. Examples
   3. Applications
3. Gradual Argumentation
   1. Model
   2. Examples
   3. Applications

 September 21 or 22, 2020.

Exact time and location will be announced soon.

Download Slides

https://sourceforge.net/projects/probabble/

https://sourceforge.net/projects/attractorproject/

[1] Anthony Hunter, Matthias Thimm:
Probabilistic Reasoning with Abstract Argumentation Frameworks. J. Artif. Intell. Res. 59: 565-611 (2017).

[2] Leila Amgoud, Jonathan Ben-Naim:
Evaluation of arguments in weighted bipolar graphs. Int. J. Approx. Reason. 99: 39-55 (2018).

[3] Pietro Baroni, Antonio Rago, Francesca Toni:
From fine-grained properties to broad principles for gradual argumentation: A principled spectrum. Int. J. Approx. Reason. 105: 252-286 (2019).

[4] Anthony Hunter, Lisa Andreevna Chalaguine, Tomasz Czernuszenko, Emmanuel Hadoux, Sylwia Polberg:
Towards Computational Persuasion via Natural Language Argumentation Dialogues. KI 2019: 18-33.

[5] Oana Cocarascu, Antonio Rago, Francesca Toni:
Extracting Dialogical Explanations for Review Aggregations with Argumentative Dialogical Agents. AAMAS 2019: 1261-1269.

[6] Antonio Rago, Oana Cocarascu, Francesca Toni:
Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them. IJCAI 2018: 1949-1955.