Distributed Search Systems with Self-Adaptive Organizational Setups

TitleDistributed Search Systems with Self-Adaptive Organizational Setups
Publication TypeJournal Article
Year of Publication2017
AuthorsWall, F.
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueRegular Issue
Volume4
Number4
Date Published06/2017
Pagination88-95
Abstract

This paper studies the effects of learning-induced alterations of distributed search systems’ organizations. In particular, scenarios where alterations of the search-systems’ organizational setup are based on a form of reinforcement learning are compared to scenarios where the organizational setup is kept constant and to scenarios where the setup is changed randomly. The results indicate that learning-induced alterations may lead to high levels of performance combined with high levels of efficiency in terms of reorganization-effort. However, the results also suggest that the complexity of the underlying search problem together with the aspiration level (which drives positive or negative reinforcement) considerably shapes the effects of learning.

KeywordsAgents, Complexity, Learning, Simulation
DOI10.9781/ijimai.2017.4411
URLhttp://www.ijimai.org/journal/sites/default/files/files/2017/01/ijimai20174_4_11_pdf_14655.pdf
AttachmentSize
ijimai20174_4_11.pdf1.89 MB