Algorithm SM-17 is a modern version of the spaced repetition algorithm used in SuperMemo. It was developed, implemented, and tested in the years 2014-2016.
SuperMemo had a fertile impact on the research in modeling long-term memory. One of the most interesting fruits of that research is the two component model of memory. Over the last two decades, it has been hinted on many occasions that the model may provide a sound theoretical basis for a better and more universal approach to spaced repetition. Algorithm SM-17 is a successful conversion of the theory into a practical application. Due to its universal nature, the algorithm should, in the future, find its way to all SuperMemo products.
The two component model of long-term memory underlies Algorithm SM-17. The model asserts that two variables are sufficient to describe the status of unitary memory in a healthy human brain:
In the literature on memory, an ill-defined term of memory strength will often stand for either stability (S) or retrievability (R). This results in a great deal of confusion that slows down the progress of memory research. In 1995 (and earlier), we have proven theoretically that the two variables: R and S are sufficient to describe the status of memory in spaced repetition.