New knowledge for old using the crystal learning lamp

RSCL (random seeded crystal learning) methods address the principal problem met in the design and programming of solutions to complex problems; namely, the automation of redundant tasks. Such automation entails the evolution of a rule base. The problem then pertains to cracking the knowledge acquisition bottleneck. RSCL methods are predicated upon the existence of symmetry in the domain universe. Symmetry facilitates the induction of new knowledge-not merely a statistical interpolation of existing data. Like neural networks, RSCL methods are best matched to parallel platforms. Eventually, so-called expert ⁿ compilers will capture all knowledge applied in the design of software. These translators acquire knowledge from the programmer and assist him/her by assuming an ever increased scope of redundant translation tasks