The Computational Logic and Argumentation group (CLArg) was formed in 2010 by Prof. Francesca Toni with the overarching objective of advancing state-of-the-art models, systems and applications of several incarnations of argumentation as broadly understood in AI.
Computational argumentation emerged in the early 90s as a knowledge representation and reasoning paradigm that subsumed many of the then-existing formalisms for non-monotonic reasoning. During the years it has evolved into a thriving research field with applications in various domains within AI.
Argument Mining is a relatively new research area at the intersection of Natural Language Processing and Knowledge Representation and Reasoning in AI. It involves the automatic detection in text of arguments, argument components, and relations between arguments and their components.
Explainable AI aims to produce explanations supporting outputs of AI systems. Explanations are useful in many ways such as increasing human trust and satisfaction, ensuring fairness of the AI, extracting learned knowledge, and enabling human-AI collaboration.
This page collects publications of all the group members. For featured publications, please see each research theme.
This page points to ongoing and past projects led by the group or with the group as a participant.
We have been developing pieces of software and creating datasets as parts of our research.
Our group members also organise Explainable AI seminar series at Imperial.