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out with the different clients that want to use the content. The client keeps a local copy of code, which may or may not be in sync with the server copy. Periodically the client and server sort out between themselves how to get their copies back in sync. As far as you, the developer, are concerned, the business of managing the source code is largely looked after by Subversion, provided that you stick to a few basic rules. These rules come down to observing a work cycle.

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that fits the learner's prior knowledge and covers the training examples Humans often make use of prior knowledge to guide the formation of new hypotheses This chapter examines purely analytical learning methods The next chapter examines combined inductive-analytical learning Explanation-based learning is a form of analytical learning in which the learner processes each novel training example by (1) explaining the observed target value for this example in terms of the domain theory, (2) analyzing this explanation to determine the general conditions under which the explanation holds, and (3) refining its hypothesis to incorporate these general conditions PROLOG-EBG an explanation-based learning algorithm that uses first-order is Horn clauses to represent both its domain theory and its learned hypotheses In PROLOG-EBG explanation is a PROLOG an proof, and the hypothesis extracted from the explanation is the weakest preimage of this proof As a result, the hypotheses output by PROLOG-EBG follow deductively from its domain theory Analytical learning methods such as PROLOG-EBG construct useful intermediate features as a side effect of analyzing individual training examples This analytical approach to feature generation complements the statistically based generation of intermediate features (eg, hidden unit features) in inductive methods such as BACKPROPAGATION Although PROLOG-EBG does not produce hypotheses that extend the deductive closure of its domain theory, other deductive learning procedures can For example, a domain theory containing determination assertions (eg, "nationality determines language") can be used together with observed data to deductively infer hypotheses that go beyond the deductive closure of the domain theory One important class of problems for which a correct and complete domain theory can be found is the class of large state-space search problems Systems such as PRODIGY SOAR have demonstrated the utility of explanationand based learning methods for automatically acquiring effective search control knowledge that speeds up problem solving in subsequent cases Despite the apparent usefulness of explanation-based learning methods in humans, purely deductive implementations such as PROLOG-EBGsuffer the disadvantage that the output hypothesis is only as correct as the domain theory In the next chapter we examine approaches that combine inductive and analytical learning methods in order to learn effectively from imperfect domain theories and limited training data.

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In a Subversion system you keep the gold-standard copy of the code in the repository on the server The first stage in getting your code under source control is to add it to the Subversion repository From that point on, any other copy is secondary Of course, you need code to work on, so you check out a local copy, called a working copy, from the repository That working copy is managed by your Subversion client (in our case, Xcode) The work cycle is simply a systematic way of ensuring that your code moves smoothly back and forth between the repository and the working copy as it needs to In practical terms it works like this: At the beginning of a piece of coding work (I tend to do this at the beginning of the working day) you update your working copy from the repository.

The roots of analytical learning methods can be traced to early work by Fikes et al (1972) on learning macro-operators through analysis of operators in ABSTRIPS and to somewhat later work by Soloway (1977) on the use of explicit prior knowledge in learning Explanation-based learning methods similar to those discussed in this chapter first appeared in a number of systems developed during the early 1980s, including DeJong (1981); Mitchell (1981); Winston et al

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