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It is important to strike a balance between accuracy – how faithfully the explanation reflects the process of the AI system – and explainability – how well end users understand the process. This is a difficult balance to strike, since the complexity of machine learning makes it difficult for even ML engineers to fully understand, let ...
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]
Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...
If meaningful learning is occurring, then the learner is fully engaged; the brain can then organize the information based on what it relates to; this creates the associations that help us learn more and understand better by making connections. [2] This also means that these facts will be remembered together, instead of individually.
Associationists understand thinking as the succession of ideas or images. They are particularly interested in the laws of association that govern how the train of thought unfolds. Behaviorists , by contrast, identify thinking with behavioral dispositions to engage in public intelligent behavior as a reaction to particular external stimuli .
Insight is the understanding of a specific cause and effect within a particular context. [citation needed] The term insight can have several related meanings: a piece of information; the act or result of understanding the inner nature of things or of seeing intuitively (called noesis in Greek) an introspection
The deductive argument is called an explanation, its premisses are called the explanans (L: explaining) and the conclusion is called the explanandum (L: to be explained). Depending on a number of additional qualifications, an explanation may be ranked on a scale from potential to true. Not all explanations in science are of the D-N type, however.
However, they note that this explanation is "very hand-wavy" and argue that a more formal explanation would be preferable. [4] Levy et al. (2015) [24] show that much of the superior performance of word2vec or similar embeddings in downstream tasks is not a result of the models per se, but of the choice of specific hyperparameters. Transferring ...