@article{2610,
  keywords = {Simulation, Learning, Agents, Complexity},
  author = {Friederike Wall},
  title = {Distributed Search Systems with Self-Adaptive Organizational Setups},
  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.},
  year = {2017},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {4},
  number = {4},
  pages = {88-95},
  month = {06/2017},
  issn = {1989-1660},
  url = {http://www.ijimai.org/journal/sites/default/files/files/2017/01/ijimai20174_4_11_pdf_14655.pdf},
  doi = {10.9781/ijimai.2017.4411},
}