Epstein Secrets

that is relatively simple, yet still intuitively rich and meaningful. Toward that end, the context of

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

that is relatively simple, yet still intuitively rich and meaningful. Toward that end, the context of

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

s. - We have not yet thought through exactly how rich the semantic formalism should be for handling th

can be assigned to these expressions. by making explicit use of the notions of conditional random fields (or rather, a certain special case, termed Markov Logic Networks). Conditional random fields, or Markov networks, are a certain mathematical formalism that provides the most general framework in which entropy max

enough, in the sense that they lack sufficiently rich and nuanced interactions HOUSE_OVERSIGHT_012945

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