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Geometric Sequential Learning Dynamics
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DOI：10.1109/LCOMM.2020.3029035.png)
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摘要
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In this letter, we introduce a novel dynamic model for predicting the exact strategies of the opponents without message exchange, namely geometric sequential learning dynamics (GSLD). The intuition is twofold; first, the utility function is widely modeled by arbitrary exponential varieties; second, the equidistant sampled exponential function comprises a geometric sequence. To validate GSLD, we model the exponential variety game (EVG) and prove its convergence by showing that it is a continuous quasi-concave game. The proposed scheme enables the construction of the exact individual utility function, which results in a faster convergence and a high utility value.
Keyword:
Games
Convergence
Wireless communication
Heuristic algorithms
Indexes
Electronic mail
Stacking
Geometric sequential learning dynamics (GSLD)
exponential variety game (EVG)
communication cost
strategy prediction
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4.4
论文数:
1.3W
被引数:
2.2W
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