Nina Gierasimczuk: From grammar inference to learning action models. A case for Explainable AI
Channel: Dipartimento di Filosofia 'Piero Martinetti'
Date: 26/08/2020

Nina Gierasimczuk - Danish Technical University
May 21, 2020, h. 17:00-19:00


Abstract: 
The endeavour of finding patterns in data spans over many different fields, from computational linguistics to self-driving cars. Machine learning is undeniably a great source of progress across the board.  In my talk I will resurrect the old way of thinking about learning, long the lines of symbolic artificial intelligence. I will report on a recent work about inferring action models from descriptions of action executions. Such a framework constitutes an informed, reliable and verifiable way of learning, in which the learner can not only classify objects correctly, but can also report on the symbolic representation she bases her conjectures on. It is nice that the ability for AI to “explain itself” in such a way is nowadays a growing demand in the community. My action models are those of Dynamic Epistemic Logic, and the methodology is automata-theoretic in spirit.


Organization: The Logic Seminar - Department of Philososophy "Piero Martinetti" - University of Milan.