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

idual cognitive processes. The CogPrime design gravitates more toward the latter approach. though also with some specific mechanisms within various MindAgents; and efforts have been made to have these specific mechanisms modulated by the generic attention allocation structures and dynamics wherever possibl

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

hich data and procedure are explicitly identical, and there is just one new active data structure in place of the distinction between AtomSpace and MindAgents Where on this continuum does the "mere learning" end and the "real self-modification" start? In this chapter we consider some mechanisms for "adva

ilation 319 schema learning the learning of schemata carrying out cognitive processes in more specialized, context-dependent ways than the general MindAgents do. Eventually, once a CogPrime instance becomes sufficiently complex and advanced, these cognitive schema may replace the MindAgents altogether, l

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

mpilation 319 schema learning the learning of schemata carrying out cognitive processes in more specialized, context-dependent ways than the general MindAgents do. Eventually, once a CogPrime instance becomes sufficiently complex and advanced, these cognitive schema may replace the MindAgents altogether, lea

which data and procedure are explicitly identical, and there is just one new active data structure in place of the distinction between AtomSpace and MindAgents Where on this continuum does the "mere learning" end and the "real self-modification" start? In this chapter we consider some mechanisms for "advan

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

Agent migration, and machines joining or leaving the CogPrime cluster. So, at the Unit level, attention allocation in CogPrime has two aspects: how MindAgents and Tasks receive attention from CogPrime, and how Atoms receive attention from different MindAgents and Tasks. The topic of this Section is the fo

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

to Atoms with more impor- tance, and hence creates an evolutionary, dynamic with importance as the fitness criterion and the whole constellation of MindAgents as the novelty-generation mechanism. However, MOSES explicitly embodies evolutionary dynamics for the learning of new patterns and procedures that

k for Natural Language Comprehension Now we move forward in the pipeline from syntax toward semantics. The NL comprehension framework provided with OpenCog at its inception in 2008 is RelEx, an English-language se- mantic relationship extractor, which consists of two main components: the dependency extra

rammar terms, this corresponds loosely to (S (NP The cat) (VP chased (NP a snake)) EFTA00624530 384 44 Natural Language Comprehension but the OpenCog linguistic pipeline makes scant use of this kind of phrase structure rendition (which is fine in this simple example; but in the case of complex sen

sambigua- tion that needs to be done to get the structure of the resultant Atom-set correct. Any further disambiguation is left to be done later, by MindAgents acting on the Atom-sets after they've already been placed in the AtomSpace. • In the old approach, the RelEx2Frame rules attempted, in many cases,

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

d effort management frameworks attempt to enforce this, via minimizing the amount of effort spent by the system in getting to a certain conclusion. MindAgents operating primarily on one kind of knowledge (e.g. MOSES, PLN) may for a time seek to follow the shortest paths within their particular correspondin

pause to briefly discuss some of the practicalities of work done with the OpenCog system currently implementing parts of the CogPrime architecture. OpenCog, the open-source software framework underlying the "OpenCogPrime" (currently partial) implementation of the CogPrime architecture, has been used for

cessful, this will provide an outstanding platform for ongoing AGI development, as well as a visually appealing and immediately meaningful demo for OpenCog. Specific, particularly simple tasks that are the focus of this project team's current work at time of writing include: • Watch another character

gh STI., which rapidly decreases in meet cases local workspaces bubbles of interlinked Atoms with moderate impor- tance, focused on by a subset of MindAgents (defined in Chapter 19 of Part 2) for a period of time perceptual associative memory HebbianLinks in the AT sensory memory spaceserver/timeserve

the present work, it has also been used to control virtual agents in virtual worlds [GEA08]. Prototype work done during 2007-2008 involved using an OpenCog variant called the Open- PetBrain to control virtual dogs in a virtual world (see Figure 6.6 for a screenshot of an OpenPetBrain-controlled virtual d

level of capability will be achievable by simply interposing DeS- TIN (or some other system in its place) as a perception /action “black box” between OpenCog and a robot. Some preliminary experiments in this direction have already been carried out, con- necting the OpenPetBrain to a Nao robot using simpler

high STI, which rapidly decreases in most cases ocal workspaces bubbles of interlinked Atoms with moderate impor- ance, focused on by a subset of MindAgents (defined in Chapter 19 of Part 2) for a period of time perceptual associative memory |HebbianLinks in the AT sensory memory spaceserver /timeserver

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

links that could simply be found by the pattern matcher when trying to find variable assignments to satisfy (El, E2, E3, E4}. 36.4.1 Breakdown into MindAgents To make this sort of PLN dynamic work, we require a number of MindAgents to be operating "ambiently" in the background whenever inference is occurr