Abstract
Modern artificial intelligence approaches study game-playing agents in multi-agent social environments, in order to better simulate the real world playing behaviors; these approaches have already produced promising results. In this paper we present the results of applying human rating systems for competitive games with social activity, to evaluate synthetic agents’ performance in multi-agent systems. The widely used Elo and Glicko rating systems are tested in large-scale synthetic multi-agent game-playing social events, and their rating outcome is presented and analyzed.
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Notes
- 1.
We are not considering here the L1 distance but rather the simple subtraction of the rankings.
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Kiourt, C., Kalles, D., Pavlidis, G. (2016). Human Rating Methods on Multi-agent Systems. In: Rovatsos, M., Vouros, G., Julian, V. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2015 2015. Lecture Notes in Computer Science(), vol 9571. Springer, Cham. https://doi.org/10.1007/978-3-319-33509-4_11
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DOI: https://doi.org/10.1007/978-3-319-33509-4_11
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