Computational argumentation: from non-monotonic reasoning to deep argumentative explanations
Computational argumentation is a well-established form of knowledge representation and reasoning with roots in non-monotonic formalisms. It is based on defining argumentation frameworks comprising sets of arguments and dialectical relations between them (e.g. of attack and, in addition or instead, of support), as well as so-called semantics (e.g. amounting to definitions of dialectically acceptable sets of arguments or of dialectical strength of arguments) with accompanying computational machinery. In this talk I will show how computational argumentation, combined with a variety of mechanisms for mining argumentation frameworks, can be used for obtaining deep explanations for the outputs of a variety of machine-learning based classifiers (e.g. based on deep learning). I will show how the resulting argumentative explanations can be 'deep', to faithfully reflect the underlying models' structure, and can support human-driven model debugging.
Francesca Toni is Professor in Computational Logic and Royal Academy of Engineering/JP Morgan Research Chair on Argumentation-based Interactive Explainable AI at the Department of Computing, Imperial College London, UK, and the founder and leader of the CLArg (Computational Logic and Argumentation) research group. Her research interests lie within the broad area of Knowledge Representation and Reasoning in AI and Explainable AI, and in particular include Argumentation, Argument Mining, Logic-Based Multi-Agent Systems, Non-monotonic/Default/
She has published over 200 papers, co-chaired ICLP2015 (the 31st International Conference on Logic Programming) and KR 2018 (the 16th Conference on Principles of Knowledge Representation and Reasoning), is corner editor on Argumentation for the Journal of Logic and Computation, in the editorial board of the Argument and Computation journal and the AI journal, and in the Board of Advisors for KR Inc. and Theory and Practice of Logic Programming.