Adapting Autonomic Electronic Institutions to Heterogeneous Agent Societies. Bou, E., López-Sánchez, M., Rodríguez-Aguilar, J. A., & Sichman, J. S. In Vouros, G., Artikis, A., Stathis, K., & Pitt, J., editors, Organized Adaption in Multi-Agent Systems, pages 18–35. Springer.
doi  abstract   bibtex   
Electronic institutions (EIs) define the rules of the game in agent societies by fixing what agents are permitted and forbidden to do and under what circumstances. Autonomic Electronic Institutions (AEIs) adapt their rules to comply with their goals when regulating agent societies composed of varying populations of self-interested agents. We present a self-adaptation model based on Case-Based Reasoning (CBR) that allows an AEI to yield a dynamical answer to changing circumstances. In order to demonstrate adaptation empirically, we consider a traffic control scenario populated by heterogeneous agents. Within this setting, we demonstrate statistically that an AEI is able to adapt to different heterogeneous agent populations.
@inproceedings{Bou2009,
  title = {Adapting {{Autonomic Electronic Institutions}} to {{Heterogeneous Agent Societies}}},
  booktitle = {Organized {{Adaption}} in {{Multi-Agent Systems}}},
  author = {Bou, Eva and López-Sánchez, Maite and Rodríguez-Aguilar, J. A. and Sichman, Jaime Simão},
  editor = {Vouros, George and Artikis, Alexander and Stathis, Kostas and Pitt, Jeremy},
  date = {2009},
  pages = {18--35},
  publisher = {Springer},
  location = {Berlin, Heidelberg},
  doi = {10.1007/978-3-642-02377-4_2},
  abstract = {Electronic institutions (EIs) define the rules of the game in agent societies by fixing what agents are permitted and forbidden to do and under what circumstances. Autonomic Electronic Institutions (AEIs) adapt their rules to comply with their goals when regulating agent societies composed of varying populations of self-interested agents. We present a self-adaptation model based on Case-Based Reasoning (CBR) that allows an AEI to yield a dynamical answer to changing circumstances. In order to demonstrate adaptation empirically, we consider a traffic control scenario populated by heterogeneous agents. Within this setting, we demonstrate statistically that an AEI is able to adapt to different heterogeneous agent populations.},
  isbn = {978-3-642-02377-4}
}

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