  Learning classifier systems (LCSs) originated from cognitive-science research
but migrated such that LCS became powerful classification techniques. Modern
LCSs can be used to extract building blocks of knowledge to solve more
difficult problems in the same or a related domain. Recent works on LCSs showed
that the knowledge reuse through the adoption of Code Fragments, GP-like
tree-based programs, into LCSs could provide advances in scaling. However,
since solving hard problems often requires constructing high-level building
blocks, which also results in an intractable search space, a limit of scaling
will eventually be reached. Inspired by human problem-solving abilities, XCSCF*
can reuse learned knowledge and learned functionality to scale to complex
problems by transferring them from simpler problems using layered learning.
However, this method was unrefined and suited to only the Multiplexer problem
domain. In this paper, we propose improvements to XCSCF* to enable it to be
robust across multiple problem domains. This is demonstrated on the benchmarks
Multiplexer, Carry-one, Majority-on, and Even-parity domains. The required base
axioms necessary for learning are proposed, methods for transfer learning in
LCSs developed and learning recast as a decomposition into a series of
subordinate problems. Results show that from a conventional tabula rasa, with
only a vague notion of what subordinate problems might be relevant, it is
possible to capture the general logic behind the tested domains, so the
advanced system is capable of solving any individual n-bit Multiplexer, n-bit
Carry-one, n-bit Majority-on, or n-bit Even-parity problem.
