Principles of artificial intelligence
Principles of artificial intelligence
The role of explicit contextual knowledge in learning concepts to improve performance
The role of explicit contextual knowledge in learning concepts to improve performance
Logic for Problem Solving
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
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Explanation-Based Generalization (EBG) has been recently a much-explored method of generalization. By utilizing domain knowledge, and knowledge of the concept being learned, EBG produced a valid generalization from a single example. Most EBG systems are currently provided with the concept being learned - or target concept- as a fixed input. A more robust generalization mechanism needs the ability to automatically formulate appropriate target concepts based on the purpose of the learning, since concepts learned for one purpose may not be appropriate for another. This paper introduced a technique and an implemented system that automatically formulate target concepts and their specialized definitions. In particular, the technique derives definitions of everyday artifacts (e.g. CUP), from information about the purpose for which agents intend to use them (e.g. to satisfy their thirst). Given two different purposes for which an agent might use a cup (e.g. as an ornament, versus to satisfy thirst), two different definitions can be derived.