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arXiv · 1909.08559

On the Right Path: A Modal Logic for Supervised Learning

Alexandru Baltag·Dazhu Li·Mina Young Pedersen
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Abstract

Formal learning theory formalizes the process of inferring a general result from examples, as in the case of inferring grammars from sentences when learning a language. Although empirical evidence suggests that children can learn a language without responding to the correction of linguistic mistakes, the importance of Teacher in many other paradigms is significant. Instead of focusing only on learner(s), this work develops a general framework---the supervised learning game (SLG)---to investigate the interaction between Teacher and Learner. In particular, our proposal highlights several interesting features of the agents: on the one hand,Learner may make mistakes in the learning process, and she may also ignore the potential relation between different hypotheses; on the other hand, Teacher is able to correct Learner's mistakes, eliminate potential mistakes and point out the facts ignored by Learner. To reason about strategies in this game, we develop a modal logic of supervised learning (SLL). Broadly, this work takes a small step towards studying the interaction between graph games, logics and formal learning theory.

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Alexandru Baltag, Dazhu Li, Mina Young Pedersen. 2019-09-18. On the Right Path: A Modal Logic for Supervised Learning. https://arxiv.org/abs/1909.08559

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