Epstein Secrets

resent 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" - A predicate comprising the definition of "block", previously imported into DeSTIN from OpenCog and

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.

ergoals like gaining knowledge are not funded as much with STI currency just now, as they are not immediately relevant. Action selection, based on ImplicationLinks derived via PLN (between various possible activ- ities and the subgoal S) causes it to start experimentally building some blocks structures. Past ex

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

or of Link2Atom. However, the ideas embodied in these rules may be useful; if cleaned up the rules might profitably be ported into the Atomspace as ImplicationLinks. • Representation Building: A stage within MOSES, wherein a candidate Combo program tree (within a deme) is modified by replacing one or more tree

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

intelligent system explicitly represents in its knowledge base, and expends some resources trying to achieve. Goal Nodes (which may be Nodes or, e.g. ImplicationLinks) are used for this purpose in OpenCog. e Goal-Driven Learning: Learning that is driven by the cognitive schematic i.e. by the quest of figuring out

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