Epstein Secrets

cognitive dynamics, especially ECAN. But then the MapEncapstdation MindAgent may do its pattern mining and recognize this map explicitly, and form a PredicateNode encapsulating it. This PredicateNode may then be used in PLN inference, conceptual blending, and so forth (e.g. helping with the formation of a con

nd seem relevant to AGI, especially advanced AGI systems capable of strong self-modification. • PLN: short for Probabilistic Logic Networks • PLN, First-Order: See First-Order hnference • PLN, Higher-Order: See Higher-Order Inference • PLN Rules: A PLN Rule takes as input one or more Atoms (the "premises"

ge over some fuzzy set of goals and environments. • Predicate Evaluation: The process of determining the Truth Value of a predicate, embod- ied in a PredicateNode. This may be recursive, as the predicate referenced internally by a Grounded PredicateNode (and represented via a Combo program tree) may itself int

nd seem relevant to AGI, especially advanced AGI systems capable of strong self-modification. • PLN: short for Probabilistic Logic Networks • PLN, First-Order: See First-Order hnference • PLN, Higher-Order: See Higher-Order Inference • PLN Rules: A PLN Rule takes as input one or more Atoms (the "premises"

ge over some fuzzy set of goals and environments. e Predicate Evaluation: The process of determining the Truth Value of a predicate, embod- ied in a PredicateNode. This may be recursive, as the predicate referenced internally by a Grounded PredicateNode (and represented via a Combo program tree) may itself inte

et and seem relevant to AGI, especially advanced AGI systems capable of strong self-modification. PLN: short for Probabilistic Logic Networks PLN, First-Order: See First-Order Inference PLN, Higher-Order: See Higher-Order Inference PLN Rules: A PLN Rule takes as input one or more Atoms (the “premises”, us