Epstein Secrets

tions, this refers to a software server that performs "offline" learning tasks (e.g. using MOSES or hilIclimbing), and is in communica- tion with an Operational Agent Controller software server that performs real-time agent control and dispatches learning tasks to and receives results from the Learning Serve

, 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

tions, this refers to a software server that performs "offline" learning tasks (e.g. using MOSES or hilIclimbing), and is in communica- tion with an Operational Agent Controller software server that performs real-time agent control and dispatches learning tasks to and receives results from the Learning Serve

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

ations, this refers to a software server that performs “offline” learning tasks (e.g. using MOSES or hillclimbing), and is in communica- tion with an Operational Agent Controller software server that performs real-time agent control and dispatches learning tasks to and receives results from the Learning Server

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