Epstein Secrets

Some Al theorists would argue that this division into "feature identification" versus "action selection" is unnecessarily artificial; for instance, Hawkins 11113011 or Arel [ARC09bl might sug- gest to use a single hierarchical neural network to do both of them. But the brain after all t in game theory,

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

KSPC13. Dimitri Kartsaklis, Mehrnoosh Sadrzadeh, Stephen Putman, and Bob Coecke. Reasoning about meaning

, 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

t Systems Workshop (PerAfIS 2008), 2008. MW07. Stephen Morgan and Christopher Winship. Counterfactuals a

, 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

ligent Systems Workshop (PerMIS 2008), 2008. W07. Stephen Morgan and Christopher Winship. Counterfactuals a

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