  Extensive-form games with imperfect recall are an important game-theoretic
model that allows a compact representation of strategies in dynamic strategic
interactions. Practical use of imperfect recall games is limited due to
negative theoretical results: a Nash equilibrium does not have to exist,
computing maxmin strategies is NP-hard, and they may require irrational
numbers. We present the first algorithm for approximating maxmin strategies in
two-player zero-sum imperfect recall games without absentmindedness. We modify
the well-known sequence-form linear program to model strategies in imperfect
recall games and use a recent technique to approximate bilinear terms. Our main
algorithm is a branch-and-bound search over these linear programs that provably
reaches a desired approximation after an exponential number of steps in the
size of the game. Experimental evaluation shows that the proposed algorithm can
approximate maxmin strategies of randomly generated imperfect recall games of
sizes beyond toy-problems within few minutes.
