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

al Linguistics. Pei34. C. Peirce. Collected papers: Volume V. Pragmatism end pragmaticism. Harvard University Press. Cambridge MA., 1934. Pel05. Martin Pelikan. Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms. Springer, 2005. PJ88a. Pearl and J. Probabilist

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

pringer, 1982. Pei34. C. Peirce. Collected papers: Volume V. Pragmatism and pragmaticism. Harvard University Press. Cambridge MA., 1934. Pe105. Martin Pelikan. Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms. Springer, 2005. Pen96. Roger Penrose. Shadows o

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

nce. Springer, 1982. Pei34. C, Peirce. Collected papers: Volume V. Pragmatism and pragmaticism. Harvard University Press. Cambridge MA., 1934. Pel05. Martin Pelikan. Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms. Springer, 2005. Pen96. Roger Penrose. Shadows of t