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Routledge Creating A Memory of Causal Relationships: An Integration of Empirical and Explanation-based Learning Methods

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Description

This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp. Review "...the best source for learning about OCCAM...a clear and detailed description of OCCAM ....incorporates many interesting and novel ideas. The ideas that are emphasized by Pazzani, and that are explored in depth in the book, are a novel integration of explanation-based and similarity-based learning methods..."—Machine Learning

Product Specifications

Format
Paperback
Domain
Amazon UK
Release Date
17 October 2016
Listed Since
21 November 2016

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