VISER: A Tractable Solution Concept for Games with Information Asymmetry
This provides a tractable method for security games and adversarial multi-agent reinforcement learning, addressing a known bottleneck in game theory.
The paper tackles the problem of predicting outcomes in games with information asymmetry, where one player lacks full payoff knowledge, by proposing the VISER solution concept, which allows independent polynomial-time computation of strategies via linear programming and extends to Markov games.
Many real-world games suffer from information asymmetry: one player is only aware of their own payoffs while the other player has the full game information. Examples include the critical domain of security games and adversarial multi-agent reinforcement learning. Information asymmetry renders traditional solution concepts such as Strong Stackelberg Equilibrium (SSE) and Robust-Optimization Equilibrium (ROE) inoperative. We propose a novel solution concept called VISER (Victim Is Secure, Exploiter best-Responds). VISER enables an external observer to predict the outcome of such games. In particular, for security applications, VISER allows the victim to better defend itself while characterizing the most damaging attacks available to the attacker. We show that each player's VISER strategy can be computed independently in polynomial time using linear programming (LP). We also extend VISER to its Markov-perfect counterpart for Markov games, which can be solved efficiently using a series of LPs.