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s, or observe the consequences of various hypothetical situations. Particularly important for dealing with episodic knowledge. • Interval Algebra: Allen Interval Algebra, a mathematical theory of the relationships be- tween time intervals. CogPrime utilizes a fuzzified version of classic Interval Algebra. • IRC Learn
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"
s, or observe the consequences of various hypothetical situations. Particularly important for dealing with episodic knowledge. • Interval Algebra: Allen Interval Algebra, a mathematical theory of the relationships be- tween time intervals. CogPrime utilizes a fuzzified version of classic Interval Algebra. • IRC Learn
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"
ces, or observe the consequences of various hypothetical situations. Particularly important for dealing with episodic knowledge. e Interval Algebra: Allen Interval Algebra, a mathematical theory of the relationships be- tween time intervals. CogPrime utilizes a fuzzified version of classic Interval Algebra. e IRC Learn
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
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