Epstein Secrets

ial Intel- ligence, 54(1-2):199 - 227, 1992. PLI2. Jeremy Fishel and Gerald Loeb. Bayesian exploration for intelligent identification of textures. Frontiers in Neurorobotics 6-4, 2012. Pri98. Roy. FYieden. Physics from Fisher Information. Cambridge U. Press, 1998. PT02. G. Pauconnier and M. Turner. Th

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

-14, 1995. HC08. David Hart and Ben Goertzel. Opencog: A software framework for integrative artificial general intelligence. In ACI, volume 171 of Frontiers in Artificial Intelligence and Applications. pages 468-472. IOS Press, 2008. HHPO12. Adam Hampshire, Roger Highfield, Beth Parkin, and Adrian Owen

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

I-14, 1995. HGO8. David Hart and Ben Goertzel. Opencog: A software framework for integrative artificial general intelligence. In AGI, volume 171 of Frontiers in Artificial Intelligence and Applications, pages 468-472. IOS Press, 2008. HHPO12. Adam Hampshire, Roger Highfield, Beth Parkin, and Adrian Owen.

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