Algorithmic learning of structures

Abstract: Assume we have a class of structures closed under isomorphism. Assume further, we receive information about one of these structures step by step: finitely much information at each step. Our goal is to determine, after finitely many steps, which structure from the class we are observing. If we can reach the goal, we call the class learnable. In the talk we formalise various aspects of this problem using ideas from computable structure theory and computational learning theory. We give syntactic characterisations for several notions of learnability and apply these results to get examples of learnable and non-learnable classes of structures.

logic

Audience: researchers in the topic

Computability theory and applications

Series comments: Description: Computability theory, logic

The goal of this endeavor is to run a seminar on the platform Zoom on a weekly basis, perhaps with alternating time slots each of which covers at least three out of four of Europe, North America, Asia, and New Zealand/Australia. While the meetings are always scheduled for Tuesdays, the timezone varies, so please refer to the calendar on the website for details about individual seminars.