What permits some systems to evolve and adapt more effectively than others? Gell-Mann [3] has stressed the importance of "com-proession" for adaptive complex systems. Information about the environment is not simply recorded as a look-up table, but is rather compressed in a theory or schema. Several conjectures are proposed: (I) compression aids in generalization; (II) compression occurs more easily in a "smooth", as opposed to a "rugged", string space; and (III) constraints from com-prossion make it likely that natural languages evolve towards smooth string spaces. We have been examining the role of such compressin for learning and evolution of formal languages by artificial agents. Our system does seem to conform generally to these expectations, but the tradeoffs between compression and the errors that sometimes accompany it need careful consideration.
展开▼
