Argumentation and Machine Learning: when the Whole is Greater than the Sum of the Parts

Speaker:  Federico Cerutti – Brescia, Italy
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing


Argumentation technology is a rich interdisciplinary area of research that has emerged as one of the most promising paradigms for common sense reasoning and conflict resolution. In this lecture, I explore the elements underpinning the vast majority of the approaches in argumentation theory: this brings to light the connections among the various disciplines involved in argumentation theory, from epistemology to law studies, to complexity theory. I discuss the most recent real-world research-grade prototypes, which present innovative ways for applying well-established theories, and enlarge the scope of applications for argumentation theory, from legal reasoning to sense-making in intelligence analysis. I illustrate how machine learning approaches help address both the knowledge acquisition problem and identify the most suitable algorithms for argumentative reasoning. Finally, I discuss the current state-of-the-art methods in machine learning using argumentation as part of their architecture. Some of them leverage argumentation technology as a regulariser in learning. Most use argumentation to support explainability and algorithmic accountability. With this lecture, the attendees will acquire an understanding of the state-of-the-art technological capabilities of argumentation technology and the synergy already envisaged between it and machine learning. This is particularly important given the current interest from research funding agencies in explainable AI.

About this Lecture

Number of Slides:  100
Duration:  120 minutes
Languages Available:  English, Italian
Last Updated: 

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