Creating A Memory of Causal Relationships

An Integration of Empirical and Explanation-based Learning Methods

Nonfiction, Health & Well Being, Psychology, Cognitive Psychology
Cover of the book Creating A Memory of Causal Relationships by Michael J. Pazzani, Taylor and Francis
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Michael J. Pazzani ISBN: 9781134992324
Publisher: Taylor and Francis Publication: March 7, 2013
Imprint: Psychology Press Language: English
Author: Michael J. Pazzani
ISBN: 9781134992324
Publisher: Taylor and Francis
Publication: March 7, 2013
Imprint: Psychology Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Taylor and Francis

Cover of the book Challenges of Labour by Michael J. Pazzani
Cover of the book Tracking the Media by Michael J. Pazzani
Cover of the book Couples on the Couch by Michael J. Pazzani
Cover of the book Climate Change Impacts on Tropical Forests in Central America by Michael J. Pazzani
Cover of the book The Routledge Handbook of Language and Intercultural Communication by Michael J. Pazzani
Cover of the book The Authoritarian Public Sphere by Michael J. Pazzani
Cover of the book Cognitive Science and Its Applications for Human-computer Interaction by Michael J. Pazzani
Cover of the book Aestheticism and the Marriage Market in Victorian Popular Fiction by Michael J. Pazzani
Cover of the book Japan in Singapore by Michael J. Pazzani
Cover of the book Leading Learning and Teaching in Higher Education by Michael J. Pazzani
Cover of the book Creativity in the Primary Curriculum by Michael J. Pazzani
Cover of the book Where are the Dead? by Michael J. Pazzani
Cover of the book Archaeology, Ritual, Religion by Michael J. Pazzani
Cover of the book The Business Guide to Sustainability by Michael J. Pazzani
Cover of the book Gimson's Pronunciation of English by Michael J. Pazzani
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy