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

emory to interact intimately, in a way that allows them to help each other squelch combinatorial explosions. This hypothesis lies at the core of the CogPrime Artificial General Intelligence design, an integrative cognitive architecture that is partially implemented in the current OpenCog software system.

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

emory to interact intimately, in a way that allows them to help each other squelch combinatorial explosions. This hypothesis lies at the core of the CogPrime Artificial General Intelligence design, an integrative cognitive architecture that is partially implemented in the current OpenCog software system.

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

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 develop its own smarts, cr