Epstein Secrets

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

r the latter are learned via MOSES or via some other means. The textual realization of Combo resembles LISP with less syntactic sugar. Internally a Combo program is represented as a program tree. • Composer: In the PLN design, a rule is denoted a composer if it needs premises for generating its conse

ve its fingers to pick up a cup, coordinating its movements with its visual perceptions. It's certainly possible to represent this sort of thing in Combo, but Combo may be an awkward tool. However, if one represents low-level procedures like this using another method, e.g. learned cell assemblies in a

the question of programs in what programming language, and in this context we have made a fairly traditional choice, using a special language called Combo that is essentially a minor variant of LISP, and supplying Combo with a set of customized primitives intended to reduce the length of the typical p

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

its own hardware and software infrastructure. CogPrime represents procedures as programs in an internal programming language called "Combo." While Combo has a textual representation, described online at the OpenCog wiki, this isn't one of its more important aspects (and may be redesigned slightly or

an internal node of a program, can also live in its own Atom. For example. a number in a program tree corresponds to a NmnberNode; an argument in a Combo program already corresponds to some Atom; and an operator in a program can be wrapped up in a Schemallode all its own, and considered as a one-leaf

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

r the latter are learned via MOSES or via some other means. The textual realization of Combo resembles LISP with less syntactic sugar. Internally a Combo program is represented as a program tree. • Composer: In the PLN design, a rule is denoted a composer if it needs premises for generating its conse

p the rules might profitably be ported into the Atomspace as ImplicationLinks. • Representation Building: A stage within MOSES, wherein a candidate Combo program tree (within a deme) is modified by replacing one or more tree nodes with alternative tree nodes, thus obtaining a new, different candidate

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

d up the rules might profitably be ported into the Atomspace as ImplicationLinks. Representation Building: A stage within MOSES, wherein a candidate Combo program tree (within a deme) is modified by replacing one or more tree nodes with alternative tree nodes, thus obtaining a new, different candidate p

her the latter are learned via MOSES or via some other means. The textual realization of Combo resembles LISP with less syntactic sugar. Internally a Combo program is represented as a program tree. e Composer: In the PLN design, a rule is denoted a composer if it needs premises for generating its conseq