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Explanation-Based Learning represents a powerful approach in AI that emphasizes understanding and generalization from minimal examples. By leveraging domain knowledge and focusing on the essential features of an example, EBL can efficiently learn and apply concepts to new situations.
Explanation-based learning in artificial intelligence is a problem-solving method that involves agent learning by analyzing specific situations and connecting them to previously acquired information. Also, the agent applies what he has learned to solve similar issues.
Explanation-based learning (EBL) is a form of machine learning that exploits a very strong, or even perfect, domain theory (i.e. a formal theory of an application domain akin to a domain model in ontology engineering, not to be confused with Scott's domain theory) in order to make generalizations or form concepts from training examples. [1]
Explanation-Based Learning (EBL) is a principled method for exploiting available domain knowledge to improve supervised learning. Improvement can be in speed of learning, confidence of learning, accuracy of the learned concept, or a combination of these.
Explanation Based Learning in Artificial Intelligence is a machine learning approach where the system learns by analyzing specific examples and forming generalizations based on these...
Explainable Artificial Intelligence refers to developing artificial intelligence models and systems that can provide clear, understandable, and transparent explanations for their decisions and predictions.
Explanation-based learning (EBL) enhances problem-solving capabilities in artificial intelligence (AI) systems. By providing meaningful explanations for the solutions generated, EBL enables machines to understand the reasoning behind their decisions and actions.
Explanation-based learning is an exciting, fresh, and promising new approach in machine learning. I believe it will play an increasingly important role both in AI research and in AI applications systems.
The reason is that EXplainable Artificial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency and fairness.
Explanation-based learning (EBL) is a technique by which an intelligent system can learn by observing examples. EBL systems are characterized by the ability to create justified generalizations from single training instances.