Epstein Secrets

HOE Higher-Order Inference • HOPLN: Higher-Order PLN • HR: Historical Repository (in a GOLEM context) • HTM: Hierarchical Temporal Memory • IA: (Allen) Interval Algebra (an algebra of temporal intervals) • IRC: Imitation / Reinforcement Correction (Learning) • LIFES: Learning-Integrated Feature S

, 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

HOE Higher-Order Inference • HOPLN: Higher-Order PLN • HR: Historical Repository (in a GOLEM context) • HTM: Hierarchical Temporal Memory • IA: (Allen) Interval Algebra (an algebra of temporal intervals) • IRC: Imitation / Reinforcement Correction (Learning) • LIFES: Learning-Integrated Feature S

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

amming HOI: Higher-Order Inference HOPLN: Higher-Order PLN HR: Historical Repository (in a GOLEM context) HTM: Hierarchical Temporal Memory IA: (Allen) Interval Algebra (an algebra of temporal intervals) IRC: Imitation / Reinforcement / Correction (Learning) LIFES: Learning-Integrated Feature Sele

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