TY - JOUR
KW - Simulation
KW - Learning
KW - Agents
KW - Complexity
AU - Friederike Wall
AB - 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.
IS - Regular Issue
M1 - 4
N2 - 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.
PY - 2017
SP - 88
EP - 95
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Distributed Search Systems with Self-Adaptive Organizational Setups
UR - http://www.ijimai.org/journal/sites/default/files/files/2017/01/ijimai20174_4_11_pdf_14655.pdf
VL - 4
SN - 1989-1660
ER -