Epstein Secrets

238 33.6.2 Identifying Hierarchical Structure In Combo trees via Metallodes and Dimensional Embedding 239 33.7 Fitness Function Estimation via Integrative Intelligence 242 Section V Declarative Learning 34 Probabilistic Logic Networks 247 34.1 Introduction 247 34.2 A Simple Overview of PLN 248 34.2.1

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,

cal breakdowns without using inference to take context into account, involves intractably large search spaces. 33.7 Fitness Function Estimation via Integrative Intelligence If instance generation is very cheap and fitness evaluation is very expensive (as is the case in many applications of evolutionary learning hi CogP

specification PredicateNode as true as possible). In evolutionary computing lingo, the specification predicate is a fitness function. Searching for PredicateNodes that embody patterns in the AtomSpace as a whole is a spe- cial case of this kind of learning, where the specification PredicateNode embodies a notio

LN, to form a classification model predicting which blocks-people will look appealing to humans, and which won't. - Fitness Function Estimation via Integrative Intelligence. To estimate the fitness of a procedure for, say, putting an arm on a blocks-built human, the agent may try out the procedure in the internal simul

. • 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