Distributed Search Systems with Self-Adaptive Organizational Setups
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 i...
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| Format: | Article |
| Language: | English |
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Universidad Internacional de La Rioja (UNIR)
2017-08-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
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| Online Access: | http://www.ijimai.org/journal/node/1541 |
| Summary: | 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. |
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| ISSN: | 1989-1660 1989-1660 |