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

/group/opencog/files, 2008. Coe08b. Ben Coertzel. A pragmatic path toward endowing virtually-embodied ais with human-level lin- guistic capability. IEEE World Congress on Computational Intelligence (Wee!), 2008. Goel0a. Ben Goertzel. Infinite-order probabilities and their application to modeling self-referential s

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

TA00624121 346 A Glossary Coe08. Ben Coertzel. A pragmatic path toward endowing virtually-embodied ais with human-level lin- guistic capability. IEEE World Congress on Computational Intelligence (WCCI), 2008. Coe09a. Ben Coertzel. Cognitive synergy: A universal principle of feasible general intelligence? In ICC

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

RSIGHT_013261 346 A Glossary Goe08. Ben Goertzel. A pragmatic path toward endowing virtually-embodied ais with human-level lin- guistic capability. IEEE World Congress on Computational Intelligence (WCCI), 2008. Goe09a. Ben Goertzel. Cognitive synergy: A universal principle of feasible general intelligence? In [CCT