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

hanahan and David A Randell. A logic-based formulation of active visual perception. In Knowledge Representation, 2004. 5S03. R. P. Salustowicz and J. Schmidhuber. Probabilistic incremental program evolution. Lecture Notes in Computer Science vol. 2706, 2003. ST91. Daniel Sleator and Davy Tetnperley. Parsing

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- ronments. Proc. ICANN'95, 1995. Sch02. Juergen Schmidhuber. Exploring the predictable.. Springer, 2002. EFTA00624125 350 A Glossary Sch06. J. Schmidhuber. Godel machines: Ashy Self-referential Optimal Universal Self-improvers. In B. Goertzel and C. Pennachin, editors, Artificial General Intelligence,

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

ments. Proc. ICANN’95, 1995. Sch02. Juergen Schmidhuber. Exploring the predictable.. Springer, 2002. HOUSE_OVERSIGHT_013265 350 A Glossary Sch06. J. Schmidhuber. Godel machines: Fully Self-referential Optimal Universal Self-improvers. In B. Goertzel and C. Pennachin, editors, Artificial General Intelligence,