Epstein Secrets

latter situation is disallowed on the software level, and is assumed in discussions not to occur. Sonic Atoms have natural names. For instance. the Schemallode corresponding to the ele- mentary schema function + may quite naturally be named "+". The NumberNode corresponding to the number .5 may naturally be

hat ExecutionLink X A B ExecutionLink2 A B means: X so that ExecutionLink A X B ExecutionLink3 A B means: X so that ExecutionLink A B X Finally, a Schemallode may be associated with a structure called a Graph. Where S is a Schemallode, Graph(S) (x,y): ExecutionLink S x y ) Sometimes, the graph of a Sche

entioned: 1. Via preprocessing of perceptual inputs (e.g. the names of NumberNode, CharacterNodes) 2. Via hard-wiring of names for Schemallodes and PredicateNodes corresponding to built-in elementary schema (e.g. +, AND, Say) If an Atom A has a name n in the system, we may write A.name n On the other hand,

scribed earlier. When an Atom is encountered, it looks into the AtomTable to evaluate the object. In the case that a Schema refers to an ungrounded Schemallode (that is not defined by a ComboTree as defined in Chapter 19), and an appropriate EvaluationLink value isn't in the AtomTable, there's an evaluatio

exts may be used in schema execution, but they're used only indirectly, via being passed to TruthValueEvaluators used for evaluating truth values of PredicateNodes or Com- poundTermNodes that occur internally in schemata being executed. EFTA00624272 EFTA00624273 Section III Perception and Action EFTA00624

On the other hand, given a ConceptNode C involved in inferred ExecutionLinks of the form ExecutionLink C Ai Bi i-1, .. ,n one may wish to find a Schemallode so that the corresponding Schemallode will fulfill this same set of ExecutionLinks. It may seem surprising at first that a ConceptNode might be inv

subgraphs containing Atom-valued variables . Each such subgraph may be represented as a PredicateNode, and frequent subgraph mining will find such PredicateNodes that have surprisingly high truth values when evaluated across the Atomspace. But unlike MOSES when applied as described above, such an algorithm wi

by the quest of figuring out which procedures can be expected to achieve a certain goal in a certain sort of context. • Grounded Schemallode: See Schemallode, Grounded. • Hebbian Learning: An aspect of Attention Allocation, centered on creating and updating HebbianLinks, which represent the simultaneous

. • Imitation Learning: Learning via copying what some other agent is observed to do. • Implication: Often refers to an ImplicationLink between two PredicateNodes, indicating an (extensional, intensional or mixed) logical implication. • Implicit Knowledge Representation: Representation of knowledge via having

plementation, the system can still reason about its use of different mind dynamics by considering each MindAgent as a virtual Procedure with a real Schemallode attached to it. This can lead to some valuable learning, with the obvious limitation that in this approach the system is thinking about its MindAgen

. • Imitation Learning: Learning via copying what some other agent is observed to do. • Implication: Often refers to an ImplicationLink between two PredicateNodes, indicating an (extensional, intensional or mixed) logical implication. • Implicit Knowledge Representation: Representation of knowledge via having