Epstein Secrets

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"

nandez M., and Mackie I. Efficient reductions with director strings. Evolutionary Computation, 1997. SMK12. Jeremy Stober, Risto Miikkulainen, and Benjamin Kuipers. Learning geometry from sensorimo- tor experience. In Proceedings of the First Joint Conference on Development and Learning and Epigenetic Robotics,

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"

rd, 2004. Min88. Marvin Minsky. The Society of Mind. MIT Press. 1988. Min07. Marvin Minsky. The Emotion Machine. 2007. MK07. Joseph Modayil and Benjamin Kuipers. Autonomous development of a grounded object ontology by a learning robot. AAAI-07, 2007. MK08. Jonathan Mugan and Benjamin Kuipers. Towards the a

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

e. Bradford, 2004. in88. Marvin Minsky. The Society of Mind. MIT Press, 1988. inO7. Marvin Minsky. The Emotion Machine. 2007. KO7. Joseph Modayil and Benjamin Kuipers. Autonomous development of a grounded object ontology by a learning robot. AAAI-07, 2007. KO08. Jonathan Mugan and Benjamin Kuipers. Towards the appl