Epstein Secrets

ms of physical inferences. In Pro- ceedings of the Twentieth-Sixth Annual Conference of the Cognitive Science Society. 2004. CB00. W. H. Calvin and D. Bickerton. Lingua er Machin. MIT Press, 2000. CFH97. Eliseo Clementini, Paolino Di Felice, and Daniel HernAindez. Qualitative representation of posi- tional

, 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

Science Society. 2004. Cas0i. Nick Cassimatis. Adaptive algorithmic hybrids for human-level artificial intelligence. 2007. CB00. W. H. Calvin and D. Bickerton. Lingua ex Afachina. MIT Press. 2000. CR06. Rory Conolly and Jerry Blancato. Computational modeling of the liver. NCCT BOSC Review, 2006. http:

cision graph looks like: Input: List L Output: List If ( P1 (L) ) N1(L) Else If ( P2 (L) ) N2 (L) Else If ( Pk (L) ) Nk(L) EFTA00624098 18.4 Self-Modification via Theorem-Proving 323 where the Pi are predicates, and the Ni are schemata corresponding to other nodes of the decision graph (children of the c

of the Cognitive Science Society. 2004. Nick Cassimatis. Adaptive algorithmic hybrids for human-level artificial intelligence. 2007. W.H. Calvin and D. Bickerton. Lingua ex Machina. MIT Press, 2000. Rory Conolly and Jerry Blancato. Computational modeling of the liver. NCCT BOSC Review, 2006. http://www.epa.go

nt decision graphs. The decision graph leading to maximal optimization, may not be the one that leads CogPrime cognition in optimal directions. 18.4 Self-Modification via Theorem-Proving Supercompilation is a potentially very valuable tool for self-modification. If one wants to take an existing schema and graduall