Epstein Secrets

, dealing with with a variety of general conceptual issues related to the engineering of advanced AGI, as well as presenting a brief overview of the CogPrime design for Artificial General Intelligence. Now here in Part 2 we plunge deep into the nitty-gritty, and describe the multiple aspects of the CogPr

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

cedure Learning as Program Learning 30.1 Introduction Broadly speaking, the learning of predicates and schemata (executable procedures) is done in CogPrime via a number of different methods, including for example PLN inference and concept predicatization (to be discussed in later chapters). Most of thes

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

49.6 Cognitive Processes 481 CogPrime's multi-memory design, it's natural to consider CogPrime's cognitive processes in terms of which memory subsystems they focus on (although, this is

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