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learning technique called Lazy Explanation-Based. Learning as a Explanation-Based Learning (EBL) systems learn by prov- ing to themselves that a .. file. The second move consists of the black pawn taking the white pawn (in its right diagonal position). 4. Indexes the negative plan under the negative goal, and stores it
30 Dec 1985 While it works well for a number of systems, the framework does not adequately capture certain aspects of the systems under development by the explanation-based learning group at Illinois. The primary inadequacies arise in the treatment of concept operationality, organization of knowledge into schemata,
Explanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory in order to make generalizations or form concepts from training examples. Contents. [hide]. 1 Details. 1.1 Basic formulation. 2 Application; 3 See also; 4 References. Details[edit]. An example of EBL
Form the generalized proof tree (put the rules in the tree in their original forms). • Replace variables in the conclusion of the top rule with new generalization variables. This is a “generalized expression" corresponding to the conclusion. • * Unify the current generalized expression with the conclusion of its corresponding rule.
This paper presents a method for the au- tomatic extraction of subgrammars to con- trol and speeding-up natural language gen- eration NLG. The method is based on explanation-based learning EBL. The main advantage for the proposed new method for NLG is that the complexity of the grammatical decision making
The online version of Extending Explanation-Based Learning by Generalizing the Structure of Explanations by Jude W. Shavlik on ScienceDirect.com, the world's leading platform for high quality peer-reviewed full-text books.
1 Aug 1985 Abstract. The problem of formulating general concepts from specific training examples has long been a major focus of machine learning research. While most previous research has focused on empirical methods for generalizing from a large numberof trainingexamplesusing no domain-specificknowledge,.
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Explanation-based learning (EBL) systems have established their applicability to a wide variety of tasks. However, in despite intensive research, several problems relating to explanation-based learning have remained by and large open.
CS 5751 Machine. Learning. Chapter 11 Explanation-Based. Learning. 2. The EBL Hypothesis. By understanding why an example is a member of a concept, can learn the essential properties of the concept. Trade-off the need to collect many examples for the ability to “explain" single examples (a. “domain" theory)
The paper provides an introductory survey of Explanation-Based Learning (EBL). It attempts to define EBL's position in AI by exploring its relationship to other AI techniques, including other
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