Text and argument mining

The Argument Mining module (3 cfu) provides an introduction to formal argumentation theory, a key area within symbolic AI that focuses on mimicking human reasoning and debate processes. By examining formal models of argumentation, students will learn about their applications to multi-agent systems and common-sense reasoning. By integrating argumentation theory into their studies, students gain valuable skills that are directly applicable to the development and improvement of explainable AI systems, an interdisciplinary approach that ensures that AI not only makes decisions accurately but also communicates them transparently.

The Argument Mining module (3 cfu) provides knowledge of formal argumentation theory in the areas of knowledge representation and reasoning, logic and decision-making.

This module provides basic knowledge and technical skills in the following topics:
- Abstract argumentation theory (Dung-style);
- Argumentative semantics;
- Bipolar argumentation frameworks;
- Structured argumentation frameworks;
- Weighted argumentation frameworks;
- Dynamics of argumentation.

Skills acquisition and ability to apply knowledge:

At the end of the module, students are expected to be able to:
- Define the argumentative frameworks introduced;
- Compute the main argumentative semantics;
- Evaluate a given argumentative framework;
- Formalize realistic reasoning problems in terms of argumentation frameworks.