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As we saw in the above example, PROLOG-EBG conducts a detailed analysis of individual training examples to determine how best to generalize from the specific example to a general Horn clause hypothesis The following are the key properties of this algorithm

Listing 9 4. Making the System Aware of Changes to the consultantFirstName and consultantLastNamePproperties + (NSSet *)keyPathsForValuesAffectingFullName { return [NSSet setWithObjects:@"consultantFirstName", @"consultantLastName", nil]; }

Unlike inductive methods, PROLOG-EBG produces justified general hypotheses by using prior knowledge to analyze individual examples The explanation of how the example satisfies the target concept determines which example attributes are relevant: those mentioned by the explanation The further analysis of the explanation, regressing the target concept to determine its weakest preimage with respect to the explanation, allows deriving more general constraints on the values of the relevant features Each learned Horn clause corresponds to a sufficient condition for satisfying the target concept The set of learned Horn clauses covers the positive training examples encountered by the learner, as well as other instances that share the same explanations The generality of the learned Horn clauses will depend on the formulation of the domain theory and on the sequence in which training examples are considered PROLOG-EBG implicitly assumes that the domain theory is correct and complete If the domain theory is incorrect or incomplete, the resulting learned concept may also be incorrect

There are several related perspectives on explanation-based learning that help to understand its capabilities and limitations

Now run the application again. Open the Journal Entries and Manage Consultants windows and make a change to a Consultant first name or last name Text Field. The updated result will appear immediately both in the Table View in the Manage Consultants window and in the Consultant popup on the Journal Entries window.

EBL as theory-guided generalization of examples EBL uses its given domain theory to generalize rationally from examples, distinguishing the relevant example attributes from the irrelevant, thereby allowing it to avoid the bounds on sample complexity that apply to purely inductive learning This is the algorithm perspective implicit in the above description of the PROLOG-EBG EBL as example-guided reformulation of theories The PROLOG-EBG algorithm can be viewed as a method for reformulating the domain theory into a more operational form In particular, the original domain theory is reformulated by creating rules that (a) follow deductively from the domain theory,

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Director, University Health Services University of Montana Missoula, MT 59812 Tel: (406) 555-7906 Fax: (406) 555-8291 E-mail: gackerman@uiucedu

Each of the journal entries shows the charge that the Consultant is making for that entry. It would be nice to see a running total, wouldn t it In Interface Builder, open the Journal Entries window. Add two label controls to the bottom of the window. Set the title of one of them to Total Consultant Charges:. Add a Currency Formatter to the other. Figure 9 18 shows how it should look.