Epstein Secrets

ImplicationLink_HOJ Inh SX stupid <.9> Inh SX rich <.97 EFTA00624176 30 20 Knowledge Representat

ges 51 Our goal in this chapter Ls to provide practical techniques for approximating the ideal pro- vided by algorithmic probability, based on what Pei Wang has termed the assumption of in- sufficient knowledge and resources tWan061, and assuming an AGI architecture that's at least vaguely humanlike in n

g up a block in the dark is a perfect example of rich coupling of perception and action networks. Feeli

y different yet both appear cognitively necessary. The argument for using probability in an AGI system is assailed by some AGI researchers such as Pei Wang, but we are swayed by the theoretical arguments in favor of probability theory's mathematically fundamental nature, as well as the massive demonstra

twork, in which a hierarchy exists, along with a rich network of heterarchical links joining nodes in t

achines. Oxford University Press, 2010. Wan95. P. Wang. Non-Axiomatic Reasoning System. PhD Thesis, Indiana University. Bloomington, 1995. Wan06. Pei Wang. Rigid Flexibility: The Logic of Intelligence. Springer. 2006. Was09. Mark Weser. Ethics for self-improving machines. In ACf-09, 2009. http://vimeo

etwork, in which a hierarchy exists, along with a rich network of heterarchical links joining nodes in t

Machines. Oxford University Press, 2010. Wan95. P. Wang. Non-Aziomatic Reasoning System. PhD Thesis, Indiana University. Bloomington, 1995. Wan06. Pei Wang. Rigid Flexibility: The Logic of Intelligence. Springer, 2006. Was09. Mark Waser. Ethics for self-improving machines. In AGI-09, 2009. http: //vimeo