114 6 A Brief Overview of CogPrime

114 6 A Brief Overview of CogPrime combination of explicit and implicit knowledge in the system’s knowledge graph, the synergetic interaction of different cognitive processes would not work so smoothly, and the emergence of effective high-level hierarchical, heterarchical and self structures would be less likely. 6.6 Analysis and Synthesis Processes in CogPrime We now return to CogPrime’s fundamental cognitive dynamics, using examples from the “virtual dog” application to motivate the discussion. The cognitive schematic Context \ Procedure — Goal 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, 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 contert 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. — Example: A virtual dog wants to achieve the goal G of getting food, and it knows that the procedure P of begging has been successful at this before, so it seeks a context C where begging can be expected to get it food. Probably this will be a context involving a friendly person. e PLN-based goal refinement is used to create new subgoals G to sit on the right hand side of instances of the cognitive schematic. — Example: Given that a virtual dog has a goal of finding food, it may learn a subgoal of following other dogs, due to observing that other dogs are often heading toward their food. e Concept formation heuristics are used for choosing G and for fueling goal refinement, but especially for choosing C (via providing new candidates for C). They are also used for choosing P, via a process called “predicate schematization” that turns logical predicates (declarative knowledge) into procedures. — Example: At first a virtual dog may have a hard time predicting which other dogs are going to be mean to it. But it may eventually observe common features among a number of mean dogs, and thus form its own concept of “pit bull,” without anyone ever teaching it this concept explicitly. HOUSE_OVERSIGHT_013030