A dynamic logic for learning theory

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 [17,9] with dynamic observation modalities [o]ϕ, as well as with a learning operator L( #» o ), which encodes the learner's conjecture after observing a finite sequence of data #» o . We completely axiomatise DLLT, study its expressivity and use it to characterise various notions of knowledge, belief, and learning.

Cognitive Bias and Belief Revision

“…Assigning some bias to the elements of the tests could potentially give better insights into truth-tracking. Finally, it would be very interesting to relate our results to the existing work on resource bounded belief revision in the AGM paradigm, in particular to [20], to look for expressibility results in the context of dynamic logic of learning theory (DLLT, [3]), and, last but not least, make steps towards empirical predictions for cognitive science of bias.…”