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

ior Al Programmer: OpenCog Learning Co-located with Ben, focused on learning 100% 850K Junior Al Programmer: Psi Motivation Co-located with Joscha, focused on Psi/OpenCog structures and dynamics 100% MK Game Programmer / Tester 100% $45K TOTAL $360K For an 18 month project the total c

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

ior Al Programmer: OpenCog Learning Co-located with Ben, focused on learning 100% $508 Junior Al Programmer: Psi Motivation Co-located with Joscha, focused on Psi/OpenCog structures and dynamics 100% MK Game Programmer / Tester 100% $45K TOTAL $360K For an 18 month project the total c

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

knowl- edge representation in order to do their cognitive work. Toward this end we have come to a similar conclusion to some other researchers (e.g. Joscha Bach and Stan Franklin), and con- cluded that given the strengths and weaknesses of current and near-future digital computers, a (loosely) neural-symboli

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

t definitely not limited to: Stan Franklin, Juergen Schmidhuber, Marcus Hutter, Kai-Uwe Kuehnberger, Stephen Reed, Blerim Enruli, Kristinn Thorisson, Joscha Bach, Abram Demski, Itamar Arel, Mark Waser, Randal Koene, Paul Rosenbloom, Zhongzhi Shi, Steve Omohundro, Bill Hibbard, Eray Ozkural, Brandon Rohrer, Ben