Creating A Memory of Causal Relationships

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A01=Michael J. Pazzani
act
Act Type
artificial general intelligence
Author_Michael J. Pazzani
Background Knowledge
Case Based Reasoning Systems
Cat Meowing
Category=JMR
Cd Structure
Coercion Schema
cognitive science research
computational learning theory
Dispositional Attribute
Domain Theory
EBL
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
explanation-based
failure
Foundational Examples
generalization
Generalization Rules
goal
Goal Failure
Goal STATE Type
Hypothesis Space
inductive reasoning
Inference Rule
integrated causal learning models
IPP
kidnapping
Kidnapping Incident
Kidnapping Schema
knowledge representation
learning
machine learning systems
Performance Examples
Prior Causal Knowledge
Quadratic Discriminant
rule
schema
Specific World Knowledge
Training Examples
type
Weak Method
Window Breaking

Product details

  • ISBN 9781138966918
  • Weight: 453g
  • Dimensions: 152 x 229mm
  • Publication Date: 17 Oct 2016
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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.

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