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"

ss, 2007. LNOO. George Lakoff and Rafael Nunez. Where Mathematics Comes From. Basic Books, 2000. Loo06. Moshe Looks. Competent Program Evolution. PhD Thesis, Computer Science Department, Wash- ington University, 2006. Loo07a. M. Looks. On the behavioral diversity of random programs. In Genetic and evolu

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"

Basic Books, 2000. Log07. Robert M. Logan. The Extended Mind. University of Toronto Press. 2007. Loo06. Moshe Looks. Competent Program Evolution. PhD Thesis, Computer Science Department, Wash- ington University, 2006. LRN87. John Laird, Paul Rosenbloom, and Alan Newell. Soar: An architecture for general

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

. Basic Books, 2000. Log07. Robert M. Logan. The Extended Mind. University of Toronto Press, 2007. Loo06. Moshe Looks. Competent Program Evolution. PhD Thesis, Computer Science Department, Wash- ington University, 2006. LRN87. John Laird, Paul Rosenbloom, and Alan Newell. Soar: An architecture for general