Probabilistic growth and mining of combinations: A unifying meta-algorithm for practical general intelligence

Abstract

A new conceptual framing of the notion of the general intelligence is outlined, in the form of a universal learning meta-algorithm called Probabilistic Growth and Mining of Combinations (PGMC). Incorporating ideas from logical inference systems, Solomonoff induction and probabilistic programming, PGMC is a probabilistic inference based framework which reflects processes broadly occurring in the natural world, is theoretically capable of arbitrarily powerful generally intelligent reasoning, and encompasses a variety of existing practical AI algorithms as special cases. Several ways of manifesting PGMC using the OpenCog AI framework are described. It is proposed that PGMC can be viewed as a core learning process serving as the central dynamic of real-world general intelligence; but that to achieve high levels of general intelligence using limited computational resources, it may be necessary for cognitive systems to incorporate multiple distinct structures and dynamics, each of which realizes this core PGMC process in a different way (optimized for some particular sort of sub-problem).