Epstein Secrets

the comparison of MOSES, PLN and ECAN with other roughly comparable algorithms from the literature (e.g. MOSES vs. Genetic Programming, PLN versus Markov Logic Networks, ECAN versus attractor neural nets). We have chosen not to include this sort of comparison in the core of the proposed work, because it would increa

nnachin and Geisweiller, 2014). The CogPrime AGI design comprises an ambitious theoretical framework and practical blueprint for construction of an Artificial General Intelligence system with capability at the human level and perhaps ultimately beyond, founded centrally on the cognitive synergy hypothesis. CogPrime is describ

the comparison of MOSES, PLN and ECAN with other roughly comparable algorithms from the literature (e.g. MOSES vs. Genetic Programming, PLN versus Markov Logic Networks, ECAN versus attractor neural nets). We have chosen not to include this sort of comparison in the core of the proposed work, because it would increa

nnachin and Geisweiller, 2014). The CogPrime AGI design comprises an ambitious theoretical framework and practical blueprint for construction of an Artificial General Intelligence system with capability at the human level and perhaps ultimately beyond, founded centrally on the cognitive synergy hypothesis. CogPrime is describ

is integrative in one sense or another. Estimation of Distribution Algorithms integrate proba- bilistic reasoning with evolutionary learning [Pel05]. Markov Logic Networks [RD06] integrate formal logic and probabilistic inference, as does the Probabilistic Logic Networks framework [GIGHO08] utilized in CogPrime and expl

ical conceptual issues related to the nature of intel- ligence and mind. It then sketches the broad outlines of a novel, integrative architecture for Artificial General Intelligence (AGT) called CogPrime ... and describes an approach for giving a young AGI system (CogPrime or otherwise) appropriate experience, so that it can deve