Epstein Secrets

nalty function. In the current CogPrime codebase, the algorithm in charge of calibrating the parameters of the penalty function has been written in Python. It takes in input the log of the imitation learning engine that contains the score, the size, the penalty and the fitness of all candidates explor

resent is carried out, either by - PLN inference within OpenCog, drawing the conclusion that a block is present from the exported predicates, using ImplicationLinks comprising a working definition of a "block" - A predicate comprising the definition of "block", previously imported into DeSTIN from OpenCog and

th to Superhuman AGI Fide implication Dog Dog implication NIRO (.1) !lido a Nice Implication PLN Deduction Rule harchtoclyd on C • • or Python) O PLN INFERENCE DeClarative representation of PLN deduction rule in atomspace .... •. • PIA le a set a Schema Node that can modify the

or of Link2Atom. However, the ideas embodied in these rules may be useful; if cleaned up the rules might profitably be ported into the Atomspace as ImplicationLinks. • Representation Building: A stage within MOSES, wherein a candidate Combo program tree (within a deme) is modified by replacing one or more tree

0 18 Advanced Self-Modification: A Possible Path to Superhuman AGI Fido implication — Dog implication Nice PLN Deduction Rule (hard-coded in C++ or Python) ; (8) Fido. : —— > Nice implication - implication i icati N PLN is a set Fido P = Dog Dog bal ee Nice of Schema Nodes that can modify the Atomspac

intelligent system explicitly represents in its knowledge base, and expends some resources trying to achieve. Goal Nodes (which may be Nodes or, e.g. ImplicationLinks) are used for this purpose in OpenCog. e Goal-Driven Learning: Learning that is driven by the cognitive schematic i.e. by the quest of figuring out