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Abstract: Open systems are becoming increasingly important in a variety of distributed, networked computer applications. Their characteristics, such as agent diversity, heterogeneity and fluctuation, confront multiagent learning with new challenges. This paper presents the interaction learning meta-architecture InFFrA as one possible answer to these challenges, and introduces the opponent classification heuristic ADHOC as a concrete multiagent learning method that has been designed on the basis of... (Update)
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BibTeX entry: (Update)
M. Rovatsos, G. Wei, and M. Wolf. Multiagent learning for open systems: A study in opponent classification. In Proc. of Adaptive Agents and Multiagent Systems, pages 66--87, 2003. http://citeseer.ist.psu.edu/rovatsos03multiagent.html More
@misc{ rovatsos03multiagent,
author = "M. Rovatsos and G. Wei and M. Wolf",
title = "Multiagent learning for open systems: A study in opponent classification",
text = "M. Rovatsos, G. Wei, and M. Wolf. Multiagent learning for open systems:
A study in opponent classification. In Proc. of Adaptive Agents and Multiagent
Systems, pages 66--87, 2003.",
year = "2003",
url = "citeseer.ist.psu.edu/rovatsos03multiagent.html" }
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