Resource-Constrained Knowledge Diffusion Processes Inspired by Human Peer Learning

Document Type

Conference Proceeding

Publication Date

9-28-2023

Abstract

We consider a setting where a population of artificial learners is given, and the objective is to optimize aggregate measures of performance, under constraints on training resources. The problem is motivated by the study of peer learning in human educational systems. In this context, we study natural knowledge diffusion processes in networks of interacting artificial learners. By 'natural', we mean processes that reflect human peer learning where the students' internal state and learning process is mostly opaque, and the main degree of freedom lies in the formation of peer learning groups by a coordinator who can potentially evaluate the learners before assigning them to peer groups. Among else, we empirically show that such processes indeed make effective use of the training resources, and enable the design of modular neural models that have the capacity to generalize without being prone to overfitting noisy labels.

Identifier

85175792018 (Scopus)

ISBN

[9781643684369]

Publication Title

Frontiers in Artificial Intelligence and Applications

External Full Text Location

https://doi.org/10.3233/FAIA230273

e-ISSN

18798314

ISSN

09226389

First Page

214

Last Page

222

Volume

372

Grant

997421

Fund Ref

National Science Foundation

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