Epstein Secrets

connecting CogPrime to a virtual world (such as Second Life, Multiverse, or Unity3D, to name sonic of the virtual world / gaming platforms to which OpenCog has already been connected in practice). EFTA00624278 132 26 Perceptual and Motor Hierarchies 26.3.1 Perceiving the Virtual World The most comp

can be used for color vision, and multi-sensory and perception-action integration. The approach presented here begins with two separate Al systems, OpenCog (introduced in Chapter 6.3) and DeSTIN (introduced in Chapter 4.3.1) - both currently implemented in open-source software. Here are the relevant fe

somewhat in the de- tails from the manner in which these things would be incorporated into a standalone DeSTIN, but pursuing the same concepts in an OpenCog integration context. What we describe here is a way to make a "Uniform DeSTIN", in which the internal repre- sentation of perceived visual forms is

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

ledge and intuition regarding CogPrime. For example, beyond the ideas presented here, there is a body of work on the OpenCog wiki site, and then the OpenCog codebase itself. More extensive introductory remarks may be found in Preface of Part 1, including a brief history of the book and acknowledgements

sign. No doubt it will evolve in some respects as implementation progresses; one of the principles kept in mind during the design and development of OpenCog was modular- ity, enabling substantial modifications to particular parts of the framework to occur without requiring wholesale changes throughout th

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,

N nodes are activated and exported to the OpenCog Atomspace • Recognition that a block is present 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"

ning to bridge DeSTIN and PLN Ls intended to fit. This architecture is not yet fully implemented, but is a straightforward extension of the current OpenCog architecture for processing data from game worlds IGEA08], and is scheduled for implementation later in 2013 in the course of a funded project invo

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

his nature that can be effective in this context We have not yet done any sophisticated experiments in this regard - our current experiments using OpenCog to control robots involve cruder integration of OpenCog with perceptual and motor subsystems, rather than the tight hybridization described in Chapt

obabilistic evolutionary program learn- Mg is an effective method The probabilistic evolutionary program learning method we have worked with most in OpenCog is MOSES, and significant evidence has been gathered showing it to be dramatically more effective than genetic programming on relevant classes of p

. • 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

zed Terms • Abduction: A general form of inference that goes from data describing something to a hypothesis that accounts for the data. Often in an OpenCog context, this refers to the PLN abduction rule, a specific First-Order PLN rule (If A implies C, and B implies C, then maybe A is B), which embodie

bility. EFTA00624100 Appendix A Glossary A.1 List of Specialized Acronyms This includes acronyms that are commonly used in discussing CogPrime, OpenCog and related ideas, plus sonic that occur here and there in the text for relatively ephemeral reasons. • AA: Attention Allocation • ADF: Automatica

. • 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

HOUSE_OVERSIGHT_013240 Appendix A Glossary A.1 List of Specialized Acronyms This includes acronyms that are commonly used in discussing CogPrime, OpenCog and related ideas, plus some that occur here and there in the text for relatively ephemeral reasons. AA: Attention Allocation ADF: Automatically De

alized Terms Abduction: A general form of inference that goes from data describing something to a hypothesis that accounts for the data. Often in an OpenCog context, this refers to the PLN abduction rule, a specific First-Order PLN rule (If A implies C, and B implies C, then maybe A is B), which embodies

er atoms. Grounded- PredicateNodes and GroundedSchemaNodes connect to explicitly represented procedures (sometimes in the Combo language); ungrounded PredicateNodes and SchemaNodes are abstract and, like ConceptNodes, purely characterized by their relationships. Node Probability: Many PLN inference rules rely on

igence (or any other kind of highly powerful general intelligence), the creation of novel ideas will be paramount. New idea creation takes place in OpenCog via a variety of methods - e.g. inside MOSES which creates new program trees, PLN which creates new logical relationships, ECAN which creates new a

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