  We present a robust framework with computational algorithms to support
decision makers in sequential games. Our framework includes methods to solve
games with complete information, assess the robustness of such solutions and,
finally, approximate adversarial risk analysis solutions when lacking complete
information. Existing simulation based approaches can be inefficient when
dealing with large sets of feasible decisions; the game of interest may not
even be solvable to the desired precision for continuous decisions. Hence, we
provide a novel alternative solution method based on the use of augmented
probability simulation. While the proposed framework conceptually applies to
multi-stage sequential games, the discussion focuses on two-stage sequential
defend-attack problems.
