When: Mon, August 12 2019, Morning session

Where: Macao, China, The Venetian Macao Resort Hotel, Naples 2703-2704

Who: Federico Cerutti


Short Description of the Tutorial

Argumentation technology is a rich interdisciplinary area of research that, in the last two decades, has emerged as one of the most promising paradigms for commonsense reasoning and conflict resolution in a great variety of domains to the point that it is used in actual commercial research project such as IBM Debater.

In this tutorial PhD students, early stage researchers, and machine learning experts will be introduced to (1) argumentation technology with concrete practical examples; (2) current state-of-the-art approaches of argumentation technology that leverage machine learning, from argument mining to automatic algorithm selection; and (3) current state-of-the-art approaches to machine learning that leverage argumentation technology, for instance for explainability of results coming from deep models.

Prior knowledge of argumentation theory is not required.


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 tutorial I will 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 will 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 will then discuss how machine learning approaches are useful for addressing both the knowledge acquisition problem as well as the identification of the most suitable algorithms for argumentative reasoning. The knowledge acquisition problem in argumentation theory is mostly an instance of argument mining tasks, actively studied by researchers both in the argumentation community, and in the natural language processing community. I will discuss the current stage of algorithms for computing semantics extensions—sets of collectively acceptable arguments—of argumentation frameworks, and show the results of recent investigations on the use of machine learning techniques for improving the performance of argumentation algorithms.

Finally, I will discuss the current state-of-the-art approaches 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 tutorial the attendees will acquire a deep and comprehensive understanding of the state-of-the-art of technological capabilities of argumentation technology, and of the synergy already envisaged between it and machine learning. This is particularly important given the current interest from research funding agencies in argument mining and explainable AI.