11.4 Piaget’s Stages in the Context of Uncertain Inference 195 Broadly speaking, examples of content representation schemes are predicate logic and term logic [ESOO]. Examples of uncertainty representation schemes are fuzzy logic [Zad78], imprecise probability theory [Goo86, FC86], Dempster-Shafer theory [Sha76, Kyb97], Bayesian probability theory [Kyb97], NARS [Wan95], and the Atom representation used in CogPrime, briefly alluded to in Chapter 6 above and described in depth in later chapters. Many, but not all, approaches to uncertain inference involve only a limited, weak set of in- ference rules (e.g. not dealing with complex quantified expressions). CogPrime’s PLN inference framework, like NARS and some other uncertain inference frameworks, contains uncertain in- ference rules that apply to logical constructs of arbitrary complexity. Only a system capable of dealing with constructs of arbitrary (or at least very high) complexity will have any potential of leading to human-level, human-like intelligence. The subtlest part of uncertain inference is inference control: the choice of which inferences to do, in what order. Inference control is the primary area in which human inference currently exceeds automated inference. Humans are not very efficient or accurate at carrying out inference rules, with or without uncertainty, but we are very good at determining which inferences to do and in what order, in any given context. The lack of effective, context-sensitive inference control heuristics is why the general ability of current automated theorem provers is considerably weaker than that of a mediocre university mathematics major [Mac95]. We now review the Piagetan developmental stages from the perspective of AGI systems heavily based on uncertain inference. 11.4.1 The Infantile Stage In this initial stage, the mind is able to recognize patterns in and conduct inferences about the world, but only using simplistic hard-wired (not experientially learned) inference