  We present a concrete design for Solomonoff's incremental machine learning
system suitable for desktop computers. We use R5RS Scheme and its standard
library with a few omissions as the reference machine. We introduce a Levin
Search variant based on a stochastic Context Free Grammar together with new
update algorithms that use the same grammar as a guiding probability
distribution for incremental machine learning. The updates include adjusting
production probabilities, re-using previous solutions, learning programming
idioms and discovery of frequent subprograms. The issues of extending the a
priori probability distribution and bootstrapping are discussed. We have
implemented a good portion of the proposed algorithms. Experiments with toy
problems show that the update algorithms work as expected.
