Extending Universal Intelligence Models with Formal Notion of
  Representation
  Solomonoff induction is known to be universal, but incomputable. Its
approximations, namely, the Minimum Description (or Message) Length (MDL)
principles, are adopted in practice in the efficient, but non-universal form.
Recent attempts to bridge this gap leaded to development of the
Representational MDL principle that originates from formal decomposition of the
task of induction. In this paper, possible extension of the RMDL principle in
the context of universal intelligence agents is considered, for which
introduction of representations is shown to be an unavoidable meta-heuristic
and a step toward efficient general intelligence. Hierarchical representations
and model optimization with the use of information-theoretic interpretation of
the adaptive resonance are also discussed.
