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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
heritance A Similarity A B Note that here the symmetry or otherwise of the link is not implicit in the notation. Similar- ityLinks are symmetrical, InheritanceLinks are not. When this distinction is necessary, it will be explicitly made. WIKISOURCE:FunctionNotation 20.3 Representing Functions and Predicates Sc
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
d B are characterized by relationships of the form Member C A, Member D B, etc. It is easy to see that, in the crisp case, where all MemberLinks and InheritanceLinks have strength 0 or 1, FOPLN reduces to standard propositional logic. Where inheritance is crisp but membership isn't, FOPLN reduces to higher-order
us to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its personal thoughts into the mental language of another OpenCog instance. This translation process may be quite subtle, sin
representation, with an explicit (uncertain) logical aspect as well as an explicit subsymbolic aspect more analogous to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its persona
sorimotor network will naturally possess hierarchical conceptual relationships, and will thus naturally grow hier- archical links between them (e.g. InheritanceLinks and IntensionalinheritanceLinks via PLN, AsymmetricHebbianLinks via ECAN). EFTA00624057 15 Emergent Networks of Intelligence artefact Fig. 15.2
us to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its personal thoughts into the mental language of another OpenCog instance. This translation process may be quite subtle, since
d representation, with an explicit (uncertain) logical aspect as well as an explicit subsymbolic aspect more analogous to attractor neural nets. The OpenCog design also contains a mechanism called Psynese (not yet implemented), in- tended to make it easier for one OpenCog instance to translate its persona
nsorimotor network will naturally possess hierarchical conceptual relationships, and will thus naturally grow hier- archical links between them (e.g. InheritanceLinks and IntensionalInheritanceLinks via PLN, AsymmetricHebbianLinks via ECAN). HOUSE_OVERSIGHT_013197 282 15 Emergent Networks of Intelligence Fig. 15
Entities connected to both OpenCog and InheritanceLinks

Marc Rich
PERSONCogPrime
ORGANIZATIONDeSTIN
ORGANIZATIONConceptNodes
ORGANIZATIONMindAgents
PERSON
George W. Bush
PERSON
Stephen Hawking
PERSON
Joel Pitt
PERSON
Woody Allen
PERSONHopfield
ORGANIZATION
Vince Foster
PERSON
Python
ORGANIZATIONPredicateNodes
ORGANIZATIONHebbian Learning
ORGANIZATIONVariableAtoms
ORGANIZATIONPredicateNode
LOCATIONCombo
LOCATIONStephan Vladimir Bugaj
PERSONProbabilistic Logic Networks
ORGANIZATIONBill Hibbard
PERSON