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A generalised dropout mechanism for distributed systems

Bull, Larry; Liu, Haixia

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Authors

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor

Haixia Liu



Abstract

This letter uses a modified form of the NK model introduced to explore aspects of distributed control. In particular, a previous result suggesting the use of dynamically formed subgroups within the overall system can be more effective than global control is further explored. The conditions under which the beneficial distributed control emerges are more clearly identified, and the reason for the benefit over traditional global control is suggested as a generally applicable dropout mechanism to improve learning in such systems.

Citation

Bull, L., & Liu, H. (2023). A generalised dropout mechanism for distributed systems. Artificial Life, 29(2), 146-152. https://doi.org/10.1162/artl_a_00393

Journal Article Type Article
Acceptance Date May 13, 2022
Online Publication Date Oct 24, 2022
Publication Date May 1, 2023
Deposit Date May 20, 2022
Publicly Available Date Nov 25, 2022
Journal Artificial life
Print ISSN 1064-5462
Electronic ISSN 1530-9185
Publisher Massachusetts Institute of Technology Press (MIT Press)
Peer Reviewed Peer Reviewed
Volume 29
Issue 2
Pages 146-152
DOI https://doi.org/10.1162/artl_a_00393
Keywords Rugged fitness landscape; multi-agent systems; neural networks; NKD model; search
Public URL https://uwe-repository.worktribe.com/output/9541079
Publisher URL https://direct.mit.edu/artl/article-abstract/doi/10.1162/artl_a_00393/113523/A-Generalised-Dropout-Mechanism-for-Distributed?redirectedFrom=fulltext

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