Distributed Search Systems with Self-Adaptive Organizational Setups

• Main Author: Friederike Wall
• Format: Article
• Language: English
• Published: Universidad Internacional de La Rioja (UNIR) 2017-08-01
• Series: International Journal of Interactive Multimedia and Artificial Intelligence
• Subjects: Agents; Complexity; Learning; Simulation
• Online Access: http://www.ijimai.org/journal/node/1541

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.