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and deleting rules later found to have negative utility He describes how using this kind of utility analysis to determine what should be learned and what should be forgotten significantly enhances the effectiveness of explanation-based learning in PRODIGY example, in a series of robot block-stacking problems, PRODIGY For encountered 328 opportunities for learning a new rule, but chose to exploit only 69 of these, and eventually reduced the learned rules to a set of 19, once low-utility rules were eliminated Tambe et al (1990) and Doorenbos (1993) discuss how to identify types of rules that will be particularly costly to match, as well as methods for re-expressing such rules in more efficient forms and methods for optimizing rule-matching algorithms Doorenbos (1993) describes how these methods enabled SOAR efficiently match a set of 100,000 learned rules in one problem domain, to without a significant increase in the cost of matching rules per state A second practical problem with applying explanation-based learning to learning search control is that in many cases it is intractable even to construct the explanations for the desired target concept For example, in chess we might wish to learn a target concept such as "states for which operator A leads toward the optimal solution" Unfortunately, to prove or explain why A leads toward the optimal solution requires explaining that every alternative operator leads to a less optimal outcome This typically requires effort exponential in the search depth Chien (1993) and Tadepalli (1990) explore methods for "lazy" or "incremental" explanation, in which heuristics are used to produce partial and approximate, but tractable, explanations Rules are extracted from these imperfect explanations as though the explanations were perfect Of course these learned rules may be incorrect due to the incomplete explanations The system accommodates this by monitoring the performance of the rule on subsequent cases If the rule subsequently makes an error, then the original explanation is incrementally elaborated to cover the new case, and a more refined rule is extracted from this incrementally improved explanation Many additional research efforts have explored the use of explanation-based learning for improving the efficiency of search-based problem solvers (for example, Mitchell 1981; Silver 1983; Shavlik 1990; Mahadevan et al 1993; Gervasio and DeJong 1994; DeJong 1994) Bennett and DeJong (1996) explore explanationbased learning for robot planning problems where the system has an imperfect domain theory that describes its world and actions Dietterich and Flann (1995) explore the integration of explanation-based learning with reinforcement learning methods discussed in 13 Mitchell and Thrun (1993) describe the application of an explanation-based neural network learning method (see the EBNN algorithm discussed in 12) to reinforcement learning problems. birt ean 128 Bar code EAN - 128 Font in BIRT Reports — OpenText - Forums
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Generate high quality GS1 128 ( EAN 128 ) images in Java by encoding GS1 ... Eclipse BIRT and Oracle Reports; Royalty free with the purchase or Java EAN 128 ... 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. 1 Open MyDBProject in Xcode Open MyDBProjectAppDelegateh 2 In Classes, create a new group called Model 3 Add a new Objective-C class called PhotosDAO to Model Create another Objective-C DVD-Video 12 or 8 cm 12 mm 12 m 074 m 570 to 1600 rpm Component digital MPEG-2 2+ h/side (1 layer), 4+ h/side (2 layer) Anamorphic Macrovision ~70 dB 345,600 pels (720 480) 4 Add a name, photoID, and photo property to PhotoDAOh and PhotoDAOm (Listings 16-6 birt gs1 128 Java GS1 - 128 (UCC/ EAN - 128 ) Barcodes Generator for Java
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