Epstein Secrets

hema execution in the first place. The problem of credit assignment thus becomes a problem of creating appropriate heuristics to guide inference of ImplicationLinks of the form described above. Assignment of credit is then implicit in the calculation of truth values for these links. The difficulty is that the pr

s, and a richer methodology for producing outputs. These are all not only HPANs but have a more particular structure that in Chapter 27 is called a Compositional Spatiotemporal Deep Learning Network or CSDLN. The particulars of the use of HPANs with OpenCog are perhaps best explained via enumer- ation of memory types and control operations. EFT

ergoals like gaining knowledge are not funded as much with STI currency just now, as they are not immediately relevant. Action selection, based on ImplicationLinks derived via PLN (between various possible activ- ities and the subgoal S) causes it to start experimentally building some blocks structures. Past ex

, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. • CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer c

or of Link2Atom. However, the ideas embodied in these rules may be useful; if cleaned up the rules might profitably be ported into the Atomspace as ImplicationLinks. • Representation Building: A stage within MOSES, wherein a candidate Combo program tree (within a deme) is modified by replacing one or more tree

, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. • CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer c

intelligent system explicitly represents in its knowledge base, and expends some resources trying to achieve. Goal Nodes (which may be Nodes or, e.g. ImplicationLinks) are used for this purpose in OpenCog. e Goal-Driven Learning: Learning that is driven by the cognitive schematic i.e. by the quest of figuring out

hing, by having another agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing its butt to the ground. CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern recognition network, in which each layer corresponds to a certain spatiotemporal granularity, the nodes on a given layer cor