Epstein Secrets

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

hich data and procedure are explicitly identical, and there is just one new active data structure in place of the distinction between AtomSpace and MindAgents Where on this continuum does the "mere learning" end and the "real self-modification" start? In this chapter we consider some mechanisms for "adva

ilation 319 schema learning the learning of schemata carrying out cognitive processes in more specialized, context-dependent ways than the general MindAgents do. Eventually, once a CogPrime instance becomes sufficiently complex and advanced, these cognitive schema may replace the MindAgents altogether, l

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

mpilation 319 schema learning the learning of schemata carrying out cognitive processes in more specialized, context-dependent ways than the general MindAgents do. Eventually, once a CogPrime instance becomes sufficiently complex and advanced, these cognitive schema may replace the MindAgents altogether, lea

which data and procedure are explicitly identical, and there is just one new active data structure in place of the distinction between AtomSpace and MindAgents Where on this continuum does the "mere learning" end and the "real self-modification" start? In this chapter we consider some mechanisms for "advan

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

Agent migration, and machines joining or leaving the CogPrime cluster. So, at the Unit level, attention allocation in CogPrime has two aspects: how MindAgents and Tasks receive attention from CogPrime, and how Atoms receive attention from different MindAgents and Tasks. The topic of this Section is the fo

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

d effort management frameworks attempt to enforce this, via minimizing the amount of effort spent by the system in getting to a certain conclusion. MindAgents operating primarily on one kind of knowledge (e.g. MOSES, PLN) may for a time seek to follow the shortest paths within their particular correspondin