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You already know that this is going to show both a list and the detail for each item, so in the next window of the assistant, choose Master/Detail View from the drop-down list Now let s consider the other options The first, Search Field, seems like a good choice There could eventually be many journal entries and so a Spotlight-style mechanism for filtering will be useful Check that option You will want to see the detail for each item selected, so check that box Finally, you also want to be able to add and remove entries, so check that box too Click Next In this final step of the assistant you need to specify which property fields from the journalEntry entity should be shown in the user interface In fact, you want all of these one way or another, so leave them all checked (the default).

B : Elephant ( f a t h e r s f ( f ather-f ( y ) ) ) t Elephant ( y )

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where f ather-of ( x ) is a function that refers to the individual who is the father of x Note that although B can be proven from A, there is no substitution 8 that allows B to be &subsumed by A As shown by these examples, our earlier notion of m o r e - g e n e r a l ~ h a n a is special case of 8-subsumption, which is itself a special case of entailment Therefore, searching the hypothesis space by generalizing or specializing hypotheses is more limited than searching by using general inverse entailment operators Unfortunately, in its most general form, inverse entailment produces intractable searches However, the intermediate notion of 8-subsumption provides one convenient notion that lies midway between our earlier definition of m o r e - g e n e r a l ~ h a n and entailment

Click on the Finish button, and you will see something like Figure 9 6 Well, that is seriously ugly isn t it That s the problem with auto-generated interfaces of course they are intelligent, but really not that intelligent What you have here is a collection of controls that have the appropriate connections to the controller, but the layout leaves much to be desired Also, you will see that the process of generating the controls doesn t always get it right Look at the control that the assistant has generated for the Entry Content It s an Image Well control since you specified that the attribute needed to contain binary data, the assistant (reasonably, perhaps, but incorrectly) concluded that you wanted an Image View control You ll need to change that, since you actually want to store text and images here (a Text View seems more appropriate).

Although inverse resolution is an intriguing method for generating candidate hypotheses, in practice it can easily lead to a combinatorial explosion of candidate hypotheses An alternative approach is to use inverse entailment to generate just the single most specific hypothesis that, together with the background information, entails the observed data This most specific hypothesis can then be used to bound a general-to-specific search through the hypothesis space similar to that used by FOIL, but with the additional constraint that the only hypotheses considered are hypotheses more general than this bound This approach is employed by the PROGOL system, whose algorithm can be summarized as follows: 1 The user specifies a restricted language of first-order expressions to be used as the hypothesis space H Restrictions are stated using "mode declarations,"

which enable the user to specify the predicate and function symbols to be considered, and the types and formats of arguments for each 2 PROGOL uses a sequential covering algorithm to learn a set of expressions f from H that cover the data For each example (xi, (xi))that is not yet covered by these learned expressions, it first searches for the most specific hypothesis hi within H such that (B A hi A xi)-l f ( x i ) More precisely, it approximates this by calculating the most specific hypothesis among those that entail f (xi)within k applications of the resolution rule (where k is a user-specified parameter) 3 PROGOL then performs a general-to-specific search of the hypothesis space bounded by the most general possible hypothesis and by the specific bound hi calculated in step 2 Within this set of hypotheses, it seeks the hypothesis having minimum description length (measured by the number of literals) This part of the search is guided by an A*-like heuristic that allows pruning without running the risk of pruning away the shortest hypothesis algorithm are described by Muggleton (1992, The details of the PROGOL 1995)