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Showing 1 to 11 of 11 for “"Explanation-Based Learning"”.
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Explanation-Based Learning via Constraint Posting and Propagation
Researchers in a new subfield of Machine Learning called Explanation-Based Learning have begun to utilize explanations as a basis for powerful learning strategies. The fundamental idea is that explanations can be used to focus on essentials and to strip away extraneous details--obviating the need …
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Explanation-Based Learning of Generalized Robot Assembly Plans
… the application of a recently developed machine learning technique, explanation-based learning, to the robot retraining problem. Explanation-based learning permits a system to acquire generalized problem-solving knowledge on the basis of a single observed problem-solving example. The resulting …
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An explanation-based learning approach to incremental planning
Planning is the task of finding a set of operators whose executive transforms the current world state into a world state which satisfies some goal criterion. Because many tasks involve focussed change of a world state, planning techniques are relevant to a wide variety of important AI tasks such as …
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Generalizing the Structure of Explanations in Explanation-Based Learning
Explanation-based learning is a recently developed approach to concept acquisition by computer. In this type of machine learning, a specific problem's solution is generalized into a form that can later be used to solve conceptually similar problems. A number of explanation-based generalization …
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The Integration of Explanation-Based Learning and Fuzzy Control in the Context of Software Assurance as Applied to Modular Avionics
… to demonstrate the satisfaction of this aim. Explanation-Based Learning (EBL) is proposed for the derivation of new control laws. The EBL domain theory embodies general control strategies, which are specialised to form fuzzy rules. A method for translating explanation structures into fuzzy …
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Knowledge-based learning: Integration of deductive and inductive learning for knowledge base completion
… to use all the knowledge that is available. Explanation-based learning and similarity-based learning operate over a domain theory and a set of examples, respectively, but neither approach makes extensive use of both forms of knowledge. Many problems in engineering and other areas can provide …
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IL(aleph): A unified approach to integrated learning
Previous efforts to integrate Explanation-Based Learning (EBL) and Similarity-Based Learning (SBL) have treated these two methods as distinct interactive processes. In contrast, the synthesis presented here views these techniques as emergent properties of a local associative learning rule operating …
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Explanation-Based Feature Construction
… and approximate, can be utilized by the learning system. Robustness is achieved by incorporating this prior knowledge in a task-specific manner, guided by the actual training examples. These goals are realized with Explanation-Based Learning (EBL). The EBL paradigm provides the necessary …
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Toward automatic model adaptation for structured domains
In order for a machine learning effort to succeed, an appropriate model must be chosen. This is a difficult task in which one must balance flexibility, so that the model can capture the complexities of the domain, and simplicity, so that the model does not overfit to irrelevant characteristics of …
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Use of prior knowledge in classification of similar and structured objects
Statistical machine learning has achieved great success in many fields in the last few decades. However, there remain classification problems that computers still struggle to match human performance. Many such problems share the same properties---large within class variability and complex structure …