198 11 Stages of Cognitive Development tomized logical rule-bases to find specialized solutions that solve only this problem fail to fully address the issue, because these solutions don’t create knowledge adequate to aid with the solution of related sorts of problems. We hypothesize that this problem is hard enough that for an inference-based AGI system to solve it in a developmentally useful way, its inferences must be guided by meta-inferential lessons learned from prior similar problems. When approaching a number conservation problem, for example, a reasoning system might draw upon past experience with set-size problems (which may be trial-and-error experience). This is not a simple “machine learning” approach whose scope is restricted to the current problem, but rather a heuristically guided approach which (a) ageregates information from prior experience to guide solution formulation for the problem at hand, and (b) adds the present experience to the set of relevant information about quantification problems for future refinement of thinking. eS ee ee ee eee eee eee —f__ Fig. 11.6: Conservation of Number For instance, a very simple context-specific heuristic that a system might learn would be: “When evaluating the truth value of a statement related to the number of objects in a set, it is generally not that useful to explore branches of the backwards-chaining search tree that contain relationships regarding the sizes, masses, or other physical properties of the objects in the set.” This heuristic itself may go a long way toward guiding an inference process toward a correct solution to the problem—but it is not something that a mind needs to know “a priori.” A concrete-operational stage mind may learn this by data-mining prior instances of inferences involving sizes of sets. Without such experience-based heuristics, the search tree for such a problem will likely be unacceptably large. Even if it is “solvable” without such heuristics, the solutions found may be