A Dynamic Logic for Learning Theory

被引：4

作者：

Baltag, Alexandru ^{[1
]}

Gierasimczuk, Nina ^{[2
]}

Ozgun, Aybuke ^{[1
,3
]}

Sandoval, Ana Lucia Vargas ^{[1
]}

Smets, Sonja ^{[1
]}

机构：

[1] Univ Amsterdam, ILLC, Amsterdam, Netherlands

[2] Tech Univ Denmark, DTU Compute, Copenhagen, Denmark

[3] Univ Lorraine, LORIA, CNRS, Nancy, France

来源：

DYNAMIC LOGIC: NEW TRENDS AND APPLICATIONS | 2018年 / 10669卷

基金：

欧洲研究理事会;

关键词：

Learning theory; Dynamic epistemic logic; Modal Logic; Subset Space Semantics; Inductive knowledge; Epistemology;

D O I：

10.1007/978-3-319-73579-5_3

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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 [9,17] 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.

引用

页码：35 / 54

页数：20

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