Professor in Computational Logic and Deputy Head in the Department of Computing, Imperial College London, UK, founder/leader of the CLArg (Computational Logic and Argumentation) research group.
Her research interests lie within the broad area of KR and Explainable AI, and in particular include Argumentation, Argument Mining, Logic-Based Multi-Agent Systems, Logic Programming, Non-monotonic/Default/Defeasible Reasoning. She graduated, summa cum laude, in Computing at the University of Pisa, Italy, in 1990, and received her PhD in Computing in 1995, from Imperial College London. She has coordinated two EU projects, received funding from EPSRC (in the UK) and the EU, and awarded a Senior Research Fellowship from The Royal Academy of Engineering and the Leverhulme Trust. She is currently Technical Director of the ROAD2H EPSRC-funded project and co-Director for the Centres of Doctoral Training in Safe and Trusted AI and in AI for Healthcare. She has published over 200 papers and co-chaired ICLP2015 (the 31st International Conference on Logic Programming) and KR 2018 (the 16th Conference on Principles of Knowledge Representation and Reasoning). She is a member of the steering committee of and KR, corner editor on Argumentation for the Journal of Logic and Computation, and in the editorial board of the Argument and Computation journal and the AI journal.
From Computational Argumentation to Explanation
Computational argumentation is a field in (symbolic AI) focusing 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, satisfying desirable dialectical properties such as that supports against an argument should strengthen it). In this talk I will overview recent efforts deploying computational argumentation to obtain and deliver to users explanations of different formats for a variety of systems, including recommenders, classifiers and schedulers.