Theory-based Bayesian models of inductive learning and reasoning
Theory-based Bayesian models of inductive learning and reasoning
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DOI：
10.1016/j.tics.2006.05.009
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发表时间：
2006-07-01
影响因子：
19.9
通讯作者：
Kemp, Charles
中科院分区：
文献类型：
--
作者：
Tenenbaum, Joshua B.;Griffiths, Thomas L.;Kemp, Charles
归纳推理允许人类在学习词义、未观察到的属性、因果关系和世界的许多其他方面时，从稀疏的数据中做出强有力的概括。传统的归纳法要么强调统计学习的力量，要么强调来自结构化领域知识、直觉理论或图式的强约束的重要性。我们认为，这两个组成部分是必要的，以解释人类知识的性质，使用和获取，我们引入了一个基于理论的贝叶斯框架建模归纳学习和推理的统计推断结构化知识表示。
Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.