Epstein Secrets

. • Imitation Learning: Learning via copying what some other agent is observed to do. • Implication: Often refers to an ImplicationLink between two PredicateNodes, indicating an (extensional, intensional or mixed) logical implication. • Implicit Knowledge Representation: Representation of knowledge via having

m's memory of what it's useful to pay attention to, in what contexts. In CogPrime this is managed by the attention allocation subsystem. • Backward Chainer: A piece of software, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. • CIM-Dynamic: Concretely-Implemented Mind D

. • Imitation Learning: Learning via copying what some other agent is observed to do. • Implication: Often refers to an ImplicationLink between two PredicateNodes, indicating an (extensional, intensional or mixed) logical implication. • Implicit Knowledge Representation: Representation of knowledge via having

m's memory of what it's useful to pay attention to, in what contexts. In CogPrime this is managed by the attention allocation subsystem. • Backward Chainer: A piece of software, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. • CIM-Dynamic: Concretely-Implemented Mind D

er atoms. Grounded- PredicateNodes and GroundedSchemaNodes connect to explicitly represented procedures (sometimes in the Combo language); ungrounded PredicateNodes and SchemaNodes are abstract and, like ConceptNodes, purely characterized by their relationships. Node Probability: Many PLN inference rules rely on

stem’s memory of what it’s useful to pay attention to, in what contexts. In CogPrime this is managed by the attention allocation subsystem. Backward Chainer: A piece of software, wrapped in a MindAgent, that carries out back- ward chaining inference using PLN. CIM-Dynamic: Concretely-Implemented Mind Dyn