Epstein Secrets

primary author, Ben Goertzel. Ben will thus begin by expressing his thanks to his primary co-authors, Cassio Pennachin (collaborator since 1998) and Nil Geisweiller (collaborator since 2005). Without outstandingly insightful, deep-thinking colleagues like you, the ideas presented here - let alone the book itsel

overlapping communities of people over a long period of time. The vast bulk of the writing here was done by Ben Goertzel; but Cassio Pennachin and Nil Geisweiller made sufficient writing, thinking and editing contributions over the years to more than merit their inclusion of co-authors. Further, many of the c

fying architecture. For this reason and others, an increasing amount of work in the AI community these days is integrative in one sense or another. Estimation of Distribution Algorithms integrate proba- bilistic reasoning with evolutionary, learning IPe1051. Markov Logic Networks IR DWil integrate formal logic and probabilistic infe

primary author, Ben Goertzel. Ben will thus begin by expressing his thanks to his primary co-authors, Cassio Pennachin (collaborator since 1998) and Nil Geisweiller (collaborator since 2005). Without outstandingly insightful, deep-thinking colleagues like you, the ideas presented here — let alone the book itself—

le overlapping communities of people over a long period of time. The vast bulk of the writing here was done by Ben Goertzel; but Cassio Pennachin and Nil Geisweiller made sufficient writing, thinking and editing contributions over the years to more than merit their inclusion of co-authors. Further, many of the cha

ifying architecture. For this reason and others, an increasing amount of work in the AI community these days 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 inferen

oertzel, the overall work and underlying ideas have been very much a team effort, with major input from the sec- ondary authors Cassio Pennachin and Nil Geisweiller, and large contributions from various other contributors as well. Nlany chapters have specifically indicated coauthors; but the contributions from

33 Probabilistic Evolutionary Procedure Learning 215 33.1 Introduction 215 33.1.1 Explicit versus Implicit Evolution in CogPrime 217 33.2 Estimation of Distribution Algorithms 218 33.3 Competent Program Evolution via MOSES 219 33.3.1 Statics 219 33.3.2 Dynamics 222 33.3.3 Architecture 223 33.3.4 Example: Ar