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e, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. • CIM-Dynamic: Con
, 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
e, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. • CIM-Dynamic: Con
, 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
e, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. CIM-Dynamic: Concr
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
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