Epstein Secrets

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

breathe, therefore people breathe." Second: • "'Variable fulfillment query"'. Given a target Link (Atoms may be Nodes or Links) with one or more VariableAtoms among its targets, figure out what Atoms may be put in place of these VariableAtoms, so as to give the target Link a high strength* confidence (i.e.

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

nt and/or interesting sub-hypergraphs in the Atom.space. • First-Order Inference (FOI): The subset of PLN that handles Logical Links not in- volving VariableAtoms or higher-order functions. The other aspect of PLN, Higher-Order Inference, uses Truth Value formulas derived from First-Order Inference. EFTA00624

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

sibility of using higher- order logical links to join predicates without any explicit variables. Via using this format exclu- sively, one could avoid VariableAtoms entirely, using only higher-order functions in the manner EFTA00624028 13.3 Atoms: Their Types and Weights 253 of pure functional programming fo

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

ent and/or interesting sub-hypergraphs in the Atomspace. • First-Order Inference (FOI): The subset of PLN that handles Logical Links not in- volving VariableAtoms or higher-order function.s. The other aspect of PLN, Higher-Order Inference, uses Truth Value formulas derived from First-Order Inference. EFTA0062

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

sibility of using higher- order logical links to join predicates without any explicit variables. Via using this format exclu- sively, one could avoid VariableAtoms entirely, using only higher-order functions in the manner HOUSE_OVERSIGHT_013168 13.3 Atoms: Their Types and Weights 253 of pure functional progra

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

equent and/or interesting sub-hypergraphs in the Atomspace. First-Order Inference (FOI): The subset of PLN that handles Logical Links not in- volving VariableAtoms or higher-order functions. The other aspect of PLN, Higher-Order Inference, uses Truth Value formulas derived from First-Order Inference. HOUSE_OVER