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Modeling strategic conflict from a game theoretical perspective involvesdealing with epistemic uncertainty. Payoff uncertainty models are typicallyrestricted to simple probability models due to computational restrictions.Recent breakthroughs Artificial Intelligence (AI) research applied to Pokerhave resulted in novel approximation approaches such as counterfactual regretminimization, that can successfully deal with large-scale imperfect games. Bydrawing from these ideas, this work addresses the problem of arbitrarycontinuous payoff distributions. We propose a method, Harsanyi-CounterfactualRegret Minimization, to solve two-player zero-sum extensive-form games witharbitrary payoff distribution models. Given a game $\Gamma$, using a Harsanyitransformation we generate a new game $\Gamma^\#$ to which we later applyCounterfactual Regret Minimization to obtain $\varepsilon$-Nash equilibria. Weinclude numerical experiments showing how the method can be applied to apreviously published problem.

Solving zero-sum extensive-form games with arbitrary payoff uncertainty models