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Creating A Memory of Causal Relationships (An Integration of Empirical and Explanation-based Learning Methods)
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Product Details
Author:
Michael J. Pazzani
Format:
Hardcover
Pages:
360
Publisher:
Taylor & Francis (May 1, 1990)
Language:
English
ISBN-13:
9780805806298
ISBN-10:
0805806296
Weight:
29.375oz
Dimensions:
6" x 9"
File:
TAYLORFRANCIS-TayFran_260129055115792-20260129.xml
Folder:
TAYLORFRANCIS
List Price:
$52.99
Case Pack:
24
As low as:
$50.34
Publisher Identifier:
P-CRC
Discount Code:
H
Audience:
Professional and scholarly
Country of Origin:
United States
Pub Discount:
30
Imprint:
Psychology Press
Overview
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.
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.
