Specialization Versus Re-Specialization: Effects Of Hebbian Learning In A Dynamic Environment

Abstract

Specializing on a subset of tasks available within a system allows agents to more efficiently fulfill system demands. When demands change, agents need to Re-Specialize. Since Re-Specialization inherently requires undoing some prior Specialization, the opposing effort often results in agents set-ding on a worse task allocation than after Specialization, even when presented with similar demands. In this work, we demonstrate these task allocation differences by looking at how well demands are fulfilled, as well as how much task switching is happening within the system. We analyze what causes the observed differences and discuss potential approaches to improving Re-Specialization in the future.

Publication Date

1-1-2018

Publication Title

Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, FLAIRS 2018

Number of Pages

354-359

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

85071955790 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85071955790

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