  Correlated Equilibrium is a solution concept that is more general than Nash
Equilibrium (NE) and can lead to outcomes with better social welfare. However,
its natural extension to the sequential setting, the \textit{Extensive Form
Correlated Equilibrium} (EFCE), requires a quadratic amount of space to solve,
even in restricted settings without randomness in nature. To alleviate these
concerns, we apply \textit{subgame resolving}, a technique extremely successful
in finding NE in zero-sum games to solving general-sum EFCEs. Subgame resolving
refines a correlation plan in an \textit{online} manner: instead of solving for
the full game upfront, it only solves for strategies in subgames that are
reached in actual play, resulting in significant computational gains. In this
paper, we (i) lay out the foundations to quantify the quality of a refined
strategy, in terms of the \textit{social welfare} and \textit{exploitability}
of correlation plans, (ii) show that EFCEs possess a sufficient amount of
independence between subgames to perform resolving efficiently, and (iii)
provide two algorithms for resolving, one using linear programming and the
other based on regret minimization. Both methods guarantee \textit{safety},
i.e., they will never be counterproductive. Our methods are the first time an
online method has been applied to the correlated, general-sum setting.
