  Online game playing algorithms produce high-quality strategies with a
fraction of memory and computation required by their offline alternatives.
Continual Resolving (CR) is a recent theoretically sound approach to online
game playing that has been used to outperform human professionals in poker.
However, parts of the algorithm were specific to poker, which enjoys many
properties not shared by other imperfect information games. We present a
domain-independent formulation of CR applicable to any two-player zero-sum
extensive-form games that works with an abstract resolving algorithm. We
further describe and implement its Monte Carlo variant (MCCR) which uses Monte
Carlo Counterfactual Regret Minimization (MCCFR) as a resolver. We prove the
correctness of CR and show an $O(T^{-1/2})$-dependence of MCCR's exploitability
on the computation time. Furthermore, we present an empirical comparison of
MCCR with incremental tree building to Online Outcome Sampling and
Information-set MCTS on several domains.
