Technical excerpt on cognitive schematics and AI learning frameworks

8.5 The Cognitive Schematic 151 Map Toreanon Goal system Semnorimotr pattern Pecogmition Map tormanion ! Map formation may No significant Ao mignsicant cirecr ayrengy foe nding mapE. dine! ayrakgy rela wubpoule, “1 col i i iormied for ir We wierd fk : aero Concepis jon Senscaimator pabiern recognition real pensorimotor al 8.5 The Cognitive Schematic Now we return to the “cognitive schematic” notion, according to which various cognitive pro- cesses involved in intelligence may be understood to work together via the implication Context \ Procedure - Goal < p > (summarized C A P > G). Semi-formally, this implication may be interpreted to mean: “If the context C’ appears to hold currently, then if I enact the procedure P, I can expect to achieve the goal G with certainty p.” The cognitive schematic leads to a conceptualization of the internal action of an intelligent system as involving two key categories of learning: e Analysis: Estimating the probability p of a posited C A P > G relationship e Synthesis: Filling in one or two of the variables in the cognitive schematic, given as- sumptions regarding the remaining variables, and directed by the goal of maximizing the probability of the cognitive schematic More specifically, where synthesis is concerned, some key examples are: e The MOSES probabilistic evolutionary program learning algorithm is applied to find P, given fixed C' and G. Internal simulation is also used, for the purpose of creating a simulation embodying C and seeing which P lead to the simulated achievement of G. — Example: A virtual dog learns a procedure P to please its owner (the goal G) in the conterzt C where there is a ball or stick present and the owner is saying “fetch”. e PLN inference, acting on declarative knowledge, is used for choosing C, given fixed P and G (also incorporating sensory and episodic knowledge as appropriate). Simulation may also be used for this purpose. HOUSE_OVERSIGHT_013067