Epstein Secrets

. PLN, a probabilistic logic engine; MOSES, an evolutionary program learning algorithm; ECAN, a nonlinear-dynamical attention allocation framework; DeSTIN, a deep learning perception algorithm) within a human-like cognitive architecture. The integration of these multiple learning algorithms is achieved

ntend our virtual parrot to do. A high level depiction of some of the learning that would be expected to occur in this case is given in Diagram 2. Program Learning (MOSES) Learns procedure to "get food from a difficult spot using a Wier Sends procedure to PLN for further learning. The action "pick up stick" w

. PLN, a probabilistic logic engine; MOSES, an evolutionary program learning algorithm; ECAN, a nonlinear-dynamical attention allocation framework; DeSTIN, a deep learning perception algorithm) within a human-like cognitive architecture. The integration of these multiple learning algorithms is achieved

ntend our virtual parrot to do. A high level depiction of some of the learning that would be expected to occur in this case is given in Diagram 2. Program Learning (MOSES) Learns procedure to "get food from a difficult spot using a stick" Sends procedure to PLN for further learning. The action "pick up stick-

front of the robot, via pinching it between two fingers and then lifting it. In this case, • The visual scene, including the block, is perceived by DeSTIN; and appropriate patterns in various DeSTIN nodes are formed • Predicates corresponding to the distribution of patterns among DeSTIN nodes are acti

vations regarding the implications of this choice for procedure learning, and the formal representation of procedure learning with CogPrime. 30.1.1 Program Learning An optimization problem may be defined as follows: a solution space S is specified, together with some fitness function on solutions, where "solvin