Epstein Secrets

e, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. • CIM-Dynamic: Con

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

e, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. • CIM-Dynamic: Con

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

e, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. CIM-Dynamic: Concr

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