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In inductive learning, the learner is given a hypothesis space H from which it must select an output hypothesis, and a set of training examples D = { ( x l , ( x ~ ) ) (x,, f ( x , ) ) } where f ( x i )is the target value for the instance f , xi The desired output of the learner is a hypothesis h from H that is consistent with these training examples In analytical learning, the input to the learner includes the same hypothesis space H and training examples D as for inductive learning In addition, the learner is provided an additional input: A domain theory B consisting of background knowledge that can be used to explain observed training examples The desired output of ,the learner is a hypothesis h from H that is consistent with both the training examples D and the domain theory B

This method s first parameter is the data to deserialize The method s second parameter indicates if the properties should be immutable or mutable The method s third parameter indicates the format to make the property list, and the fourth parameter is the error description Valid values for the second parameter are NSPropertyListImmutable, NSPropertyListMutable Containers, and NSPropertyListMutableContainersAndLeaves Valid values for the third parameter are NSPropertyListOpenStepFormat, NSPropertyListXMLFormat_v1_0, and NSPropertyListBinaryFormat_v1_0 Note that as with the dataFromPropertyList: method, should something fail, you must release the NSString holding the error description

7 6 5

.

Do not take this task s more complex data structure as implying you cannot use a property list object s writeToFile: or writeToURL: method to persist complex data structures You can, provided all items in a data structure are a property list object For instance, if an NSArray s elements each contained an NSDictionary, you could serialize the entire data structure at once by writing the NSArray to a file

4 3 2

1 Create a new View-based Application named Properties 2 Open PropertiesAppDelegatem and modify the applicationDidFinishLaunching method

6

To illustrate, in our chess example each instance xi would describe a particular chess position, and f ( x i ) would be True when xi is a position for which black will lose its queen within two moves, and False otherwise We might define the hypothesis space H to consist of sets of Horn clauses (if-then rules) as in 10, where the predicates used by the rules refer to the positions or relative positions of specific pieces on the board The domain theory B would consist of a formalization of the rules of chess, describing the legal moves, the fact that players must take turns, and the fact that the game is won when one player captures her opponent's king Note in analytical learning, the learner must output a hypothesis that is consistent with both the training data and the domain theory We say that hypothesis h is consistent with domain theory B provided B does not entail the negation of h (ie, B -h) This additional constraint that the output hypothesis must be consistent with B reduces the ambiguity faced by the learner when the data alone cannot resolve among all hypotheses in H The net effect, provided the domain theory is correct, is to increase the accuracy of the output hypothesis Let us introduce in detail a second example of an analytical learning problem--one that we will use for illustration throughout this chapter Consider an instance space X in which each instance is a pair of physical objects Each of the two physical objects in the instance is described by the predicates Color, Volume, Owner, Material, Type, and Density, and the relationship between the two objects is described by the predicate On Given this instance space, the task is to learn the target concept "pairs of physical objects, such that one can be stacked safely on the other," denoted by the predicate SafeToStack(x,y) Learning this target concept might be useful, for example, to a robot system that has the task of storing various physical objects within a limited workspace The full definition of this analytical learning task is given in Table 111.

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