DOI
10.9781/ijimai.2017.4411
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.
Source Publication
International Journal of Interactive Multimedia and Artificial Intelligence
Recommended Citation
Wall, Friederike
(2017)
"Distributed Search Systems with Self-Adaptive Organizational Setups,"
International Journal of Interactive Multimedia and Artificial Intelligence: Vol. 4:
Iss.
4, Article 3.
DOI: 10.9781/ijimai.2017.4411
Available at:
https://ijimai.researchcommons.org/ijimai/vol4/iss4/3
