Invited Speakers

Title: Hybrid Answer Set Programming: Opportunities and Challenges

Abstract:
In the recent years, the interest in combining symbolic and sub-symbolic AI approaches has been rapidly increasing. In particular neuro-symbolic AI, in which the two approaches have been combined in a number of different ways, is in the center of attention. A natural question in this context is how answer set programs, one of the main non-monotonic rule-based formalisms in use today, may fit into this endeavor. Several authors have considered how to combine answer set programs with subsymbolic AI, specifically with (deep) neural networks, at varying levels of integration in order to facilitate semantics-enhanced applications of AI that build on subsymbolic AI such as scene classification, object tracking, or visual question answering. In this talk, we shall consider hybrid answer set programming approaches and explore opportunities and challenges for them. Notably, combining answer set programs with alternative inference approaches is not novel and has been extensively studied e.g. for logic-based ontologies. We shall also revisit lessons learnt from such work for the ongoing work on hybrid answer set programming.


  • Pierre Marquis, Univ. Artois, CNRS, CRIL / Institut Universitaire de France, Lens, France (Joint DL/NMR keynote talk)

Title: Rectifying Classifiers


Abstract:

Dealing with high-risk or safety-critical applications calls for the development of trustworthy AI systems.

Beyond prediction, such systems must offer a number of additional facilities, including explanation and verification.

The case when the prediction made is deemed wrong by an expert calls for still another operation, called rectification. Rectifying a classifier aims to guarantee that the predictions made by the classifier (once rectified) comply with the expert knowledge. Here, the expert is supposed more reliable than the predictor, but their knowledge is typically incomplete.

Focusing on Boolean classifiers, I will present rectification as a change operation. Following an axiomatic approach, I will give some postulates that must be satisfied by rectification operators. I will show that the family of rectification operators is disjoint from the family of revision operators and from the family of update operators. I will also present a few results about the computation of a rectification operation.


Prof. Marquis' talk has been sponsored by EurAI.



  • Serena Villata, CNRS - Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis, France

Title: Fallacious arguments: the Place Where Knowledge Representation and Argument Mining Meet Each Other

Abstract:
Fallacies play a prominent role in argumentation since antiquity due to their contribution to argumentation in critical thinking education. They are defined as "derailments of strategic manoeuvring", meaning speech acts that violate the rules of a rational argumentative discussion for assumed persuasive gains. These derailments are particularly significant in political discourse, and the role of fallacies is becoming even more crucial nowadays as contemporary argumentation technologies face challenging tasks as misleading and manipulative information detection in news articles and political discourse, and counter-narrative generation. In this talk, I will discuss some solutions to identify automatically fallacious arguments in political debates, focusing on the prominent role of knowledge and reasoning in this challenging task.


  • Aarti Gupta, Princeton University, NJ, US (FLoC Plenary)

Title: TBA


  • Ziyad Hanna, Cadence Design Systems, CA, US & The University of Oxford, UK (FLoC Keynote)

Title: Harnessing the Power of Formal Verification for the $Trillion Chip Design Industry