Epstein Secrets

. • 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

, Ruiting Lien, Hugo de Gar's, Shuo Chen, and hamar Arel. World survey of artificial brains, part ii: Biologically inspired cognitive architectures. Neurocomputing, April 2010. GM11108. B. Goertzel, I. Coertzel M. lkle, and A. Heljakka. Probabilistic Logic Networks. Springer, 2008. GN02. Alfonso Gerevini and

. • 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

. CP1+10. Ben Goertzel, Joel Pitt, Matthew Ikle, Casein Pennachin, and Rui Liu. Glocal memory: a design principle for artificial brains and minds. Neurocomputing, April 2010. CPPG06. Ben Goertzel, Hugo Pinto, easel° Pennachin, and Izabela Freire Goertzel. Using dependency pars- ing and probabilistic inferenc

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

1. GPIt 10. Ben Goertzel, Joel Pitt, Matthew Ikle, Cassio Pennachin, and Rui Liu. Glocal memory: a design principle for artificial brains and minds. Neurocomputing, April 2010. GPPGO06. Ben Goertzel, Hugo Pinto, Cassio Pennachin, and Izabela Freire Goertzel. Using dependency pars- ing and probabilistic inferenc