SSP-MMC-FSRS is an extended verson of SSP-MMC, which is an algorithm for minimizing the memorization cost in spaced repetiton. The core hypothesis of SSP-MMC is the learner will memorize a card forever when the stability exceeds a certain threshold. With this hypothesis, and the memory state-transition function (provided by FSRS), we can formulate the problem as a special case of the Markov Decision Process (MDP), i.e., a stochastic shortest path problem.
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Stochastic-Shortest-Path-Minimize-Memorization-Cost for FSRS