Large-scale strategic games and adversarial machine learning
T Alpcan, BIP Rubinstein, C Leckie
2016 IEEE 55th Conference on Decision and Control (CDC) | IEEE | Published : 2016
Decision making in modern large-scale and complex systems such as communication networks, smart electricity grids, and cyber-physical systems motivate novel game-theoretic approaches. This paper investigates big strategic (non-cooperative) games where a finite number of individual players each have a large number of continuous decision variables and input data points. Such high-dimensional decision spaces and big data sets lead to computational challenges, relating to efforts in non-linear optimization scaling up to large systems of variables. In addition to these computational challenges, real-world players often have limited information about their preference parameters due to the prohibit..View full abstract
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Awarded by ARC Discovery Project
Awarded by DECRA
This work was supported in part by the ARC Discovery Project DP140100819, DECRA DE160100584 and grant FLI-RFP-AI1.