Resource-Constrained Knowledge Diffusion Processes Inspired by Human Peer Learning

Ehsan Beikihassan, Amy K. Hoover, Ioannis Koutis, Ali Parviz, Niloofar Aghaieabiane

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings
EditorsKobi Gal, Kobi Gal, Ann Nowe, Grzegorz J. Nalepa, Roy Fairstein, Roxana Radulescu
PublisherIOS Press BV
Number of pages9
ISBN (Electronic)9781643684369
StatePublished - Sep 28 2023
Externally publishedYes
Event26th European Conference on Artificial Intelligence, ECAI 2023 - Krakow, Poland
Duration: Sep 30 2023Oct 4 2023

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314


Conference26th European Conference on Artificial Intelligence, ECAI 2023

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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