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Deception in Game Theory and Control: A Tutorial

K. Vamvoudakis,Filippos Fotiadis,Tamer Başar,Vijay Gupta,Jorge I. Poveda,Michael Tang,Miroslav Krstic,Quanyan Zhu

Published 2025 in American Control Conference

ABSTRACT

Deception is a key tactic for agents in adversarial environments, used to mislead opponents into adopting unaware strategies. In cyber-physical systems, for instance, deception can conceal attacks against critical infrastructure. This tutorial highlights the usefulness of deception for attacking and protecting systems against adversaries, but also as a tool to increase payoff in general game-theoretic and data-driven settings. It presents several state-of-the-art techniques for control-theoretic deception, including deception in defensive cyber-physical security, game-theoretic reinforcement learning, general multi-agent learning systems, Nash equilibrium seeking, and data-driven control. Although showcased in specific contexts, the underlying concepts and ideas that we study should be generalizable by researchers to settings beyond the scope of this tutorial.

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