A dynamic logic for learning theory

Alexandru Baltag, Nina Gierasimczuk, Aybuke Ozgun, Ana Lucia Vargas Sandoval, Sonja Smets

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Building on previous work [4,5] that bridged Formal Learning Theory and Dynamic Epistemic Logic in a topological setting, we introduce a Dynamic Logic for Learning Theory (DLLT), extending Subset Space Logics [18,10] with dynamic observation modalities [o]phi, as well as with a learning operator L((o) over right arrow), which encodes the learner's conjecture after observing a finite sequence of data (o) over right arrow. We completely axiomatise DLLT, study its expressivity and use it to characterise various notions of knowledge, belief, and learning. (C) 2019 Elsevier Inc. All rights reserved.
Original languageEnglish
Article number100485
JournalJournal of Logic and Algebraic Programming
Volume109
Number of pages20
ISSN2352-2208
DOIs
Publication statusPublished - 2019

Keywords

  • Learning theory
  • Dynamic epistemic logic
  • Modal logic
  • Subset space semantics
  • Inductive knowledge
  • Epistemology

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