Epstein Secrets

have come to the following conclusions: • for relatively straightforward procedure learning problems, hilldimbing with random restart and a strong Occam bias is an effective method • for more difficult problems that elude hinclimbing, probabilistic evolutionary program learn- Mg is an effective metho

, ImplicationLink, etc.) or a fuzzy value (as in MemberLink, EvaluationLink). • Strong Self-Modification: This is generally used as synonymous with Self-Modification, in a CogPrime context. • Subsymbolic: Involving processing of data using elements that have no correspondence to natural language terms, nor abst

ne effective approach to evolu- tionary program learning. MOSES is one good realization of this approach. e. Multistart hill-climbing, with a strong Occam prior, is a good way to handle relatively straightforward program learning problems. f. Activation spreading and Hebbian learning comprise a reason

Supercompilation 319 18.3.1 Three Aspects of Supercompilation 321 18.3.2 Supercompilation for Goal-Directed Program Modification 322 18.4 Self-Modification via Theorem-Proving 323 A Glossary 325 A.1 List of Specialized Acronyms 325 A.2 Glossary of Specialized Terms 326 References 343 EFTA006

ne effective approach to evolu- tionary program learning. MOSES is one good realization of this approach. e. Multistart hill-climbing, with a strong Occam prior, is a good way to handle relatively straightforward program learning problems. f. Activation spreading and Hebbian learning comprise a reasona

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