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 [ESO0]. Examples of uncertainty representation schemes are fuzzy logic [Zad78], imprecise probability theory [Goo86, FC86], Dempster-Shafer theory [Sha76, Kyb97], Bayesian probability theory [IKyb97], 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 control schema, along with pre-heuristic pattern mining of experiential data.
In the infantile stage an entity is able to recognize patterns in and conduct inferences about its sensory surround context (i.e., it’s “world”), but only using simplistic, hard-wired (not expe- rientially learned) inference control schemata. Preheuristic pattern-mining of experiential data is performed in order to build future heuristics about analysis of and interaction with the world.
s tasks include:
1. Exploratory behavior in which useful and useless / dangerous behavior is differentiated by both trial and error observation, and by parental guidance.
2. Development of “habits” — i.e. Repeating tasks which were successful once to determine if they always / usually are so.
3. Simple goal-oriented behavior such as “find out what cat hair tastes like” in which one must plan and take several sequentially dependent steps in order to achieve the goal.
Inference control is very simple during the infantile stage (Figure 11.4), as it is the stage during which both the most basic knowledge of the world is acquired, and the most basic of cognition and inference control structures are developed as the building block upon which will be built the next stages of both knowledge and inference control.
Another example of a cognitive task at the borderline between infantile and concrete cog- nition is learning object permanence, a problem discussed in the context of CogPrime’s prede- cessor "Novamente Cognition Engine" system in [GPSL03]. Another example is the learning of
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