Omputation and D Ecision - M Aking in L Arge E Xtensive F Orm G Ames

In this thesis, we investigate the problem of decision-making in large two-player zero-sumgames using Monte Carlo sampling and regret minimization methods. We demonstrate fourmajor contributions. The first is Monte Carlo Counterfactual Regret Minimization (MC-CFR): a generic family of sample-based algorithms that compute near-optimal equilibriumstrategies. Secondly, we develop a theory for applying counterfactual regret minimizationto a generic subset of imperfect recall games as well as a lossy abstraction mechanism forreducing the size of very large games. Thirdly, we describe Monte Carlo Minimax Search(MCMS): an adversarial search algorithm based on *-Minimax that uses sparse sampling.We then present variance reduction techniques that can be used in these settings, with afocused application to Monte Carlo Tree Search (MCTS). We thoroughly evaluate our al-gorithms in practice using several different domains and sampling strategies.