Epstein Secrets

to learn procedures that will maxi- mize some Goal whose Truth Value is estimated via an expected reward calculation (where "reward" may mean simply the Truth Value of some Atom defined as "reward"). Goal-driven learning is more general than reinforcement learning as thus defined; and the learning that CogPrime does, which

, 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

to learn procedures that will maxi- mize some Goal whose Truth Value is estimated via an expected reward calculation (where "reward" may mean simply the Truth Value of some Atom defined as "reward"). Goal-driven learning is more general than reinforcement learning as thus defined; and the learning that CogPrime does, which

. 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

to learn procedures that will maxi- mize some Goal whose Truth Value is estimated via an expected reward calculation (where *reward” may mean simply the Truth Value of some Atom defined as ”reward”). Goal-driven learning is more general than reinforcement learning as thus defined; and the learning that CogPrime does, which is

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