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Error Correction: The Specialization Theory   233

               if the maximum number of valence electrons for atoms of element M is
               N, and
               if the current number of valence electrons for X, V, is less than or equal
               to N-1.
                 then double that bond.

            After the two new conditions have been added, the rule will execute only when
            the relevant atoms have space left, so to speak, to add another valence electron.
            For carbon, it will execute only when the current number of electrons is less
            than or equal to 8 – 1 = 7. In this situation, the constraint of a maximum of 8
            valence electrons will not be violated, because after the addition, the number
            of valence electrons could at most be 8. The rule has been cured of the ten-
            dency to make this particular error.
               The new rule is not completely correct. It can lead to other types of errors.
            For example, there are many situations in which the remaining valence elec-
            trons have to be disposed of in some other way than by doubling the bonds
            between carbon atoms. The rule has not been miraculously transformed from
            incomplete to perfect in a single step. Instead, it has been cured of the ten-
            dency to causing one particular type of error. There is no guarantee that it
            does not cause other types of errors. If it does, additional conditions might
            be imposed on the action of doubling bonds between carbon atoms. In addi-
            tion, revising this one rule does not preclude other rules from generating
            other types of errors. After modest amounts of practice, HS’s set of rules for
            constructing Lewis structures made only occasional errors.


                                  Three Central Concepts

            The constraint-based theory of learning from error breaks with past think-
            ing about learning from error in three ways. First, it reinterprets declarative
            knowledge as prescriptive rather than descriptive and as consisting of con-
            straints  rather  than  of  truth-bearing  propositions.  Philosophers,  logicians
            and, more recently, artificial intelligence researchers have assumed that the
            function of declarative knowledge is to support description, inference, expla-
            nation and prediction, and the formal notion of a proposition was designed
            to support those functions. The constraint-based view instead claims that the
            function of declarative knowledge is to support judgment, and that the unit of
            declarative knowledge is the constraint. The notion that declarative knowledge
            is more normative than descriptive explains how we can catch ourselves mak-
            ing errors, why there can be art critics who cannot paint and how it is possible
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