Epstein Secrets

ion. PhD thesis, MIT, 1998. ZHIO. Ruoyu Zou and Lawrence B. Holder. Frequent subgraph mining on a single l

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

t Thought, volume 2. pages 63-78. 1990. KLH8.3. Lawrence Kohlberg, Charles Levine, and Alexandra Hewer. Mo

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

ult Thought, volume 2, pages 63-78. 1990. KLH83. Lawrence Kohlberg, Charles Levine, and Alexandra Hewer. Mo

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