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Creating a memory of causal relationships : an integration of empirical and explanation-based learning methods

Author: Michael John Pazzani
Publisher: Hillsdale, N.J. : L. Erlbaum Associates, 1990.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Pazzani, Michael John, 1958-
Creating a memory of causal relationships.
Hillsdale, N.J. : L. Erlbaum Associates, 1990
(DLC) 90034771
(OCoLC)21334444
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Michael John Pazzani
ISBN: 9781134992256 1134992254 1134992270 9781134992270
OCLC Number: 644033517
Reproduction Notes: Electronic reproduction. [S.l.] : HathiTrust Digital Library, 2010. MiAaHDL
Description: 1 online resource (350 pages) : illustrations
Details: Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
Contents: Front Cover; Title Page; Copyright; Table of Contents; Acknowledgements; 1. Introduction; 1.1. Predicting the Outcome of Events; 1.1.1. Prediction; 1.1.2. Explanation; 1.1.3. Planning; 1.1.4. Inference; 1.1.5. Knowledge of causality facilitates future learning; 1.2. Learning to Predict the Outcome of Events; 1.2.1. OCCAM; 1.2.2. Predicting the future; 1.3. Methodology; 1.4. A Preview of OCCAM'S Performance; 1.4.1. Learning about coercion; 1.4.2. Learning background knowledge to understand kidnapping; 1.4.3. Kidnapping; 1.4.4. Some complications in learning kidnapping. 1.4.5. Further specializations of kidnapping1.4.6. Robustness of OCCAM; 1.4.7. Economic sanctions; 1.4.8. Learning from economic sanction incidents; 1.4.9. Question answering; 1.5. The Learning Task and the Performance Task; 1.5.1. Input representation; 1.5.2. Relationships between events, states and goals; 1.5.3. The representation of learned knowledge: Schemata; 1.5.4. The coercion schema; 1.5.5. The performance system; 1.5.6. Finding the most specific schema; 1.5.7. The learning task; 1.6. Organization of the Book; 2. What OCCAM is up against; 2.1. Relevance of Features. 2.1.1. Prediction vs. explanation2.2. The Level of Generality of Schemata; 2.3. Combining Multiple Sources of Information; 2.3.1. The use of correlation; 2.3.2. The use of prior knowledge; 2.3.3. Intra-example relationships; 2.3.4. Integrating multiple sources of information; 2.3.5. Integrated learning: An overview of OCCAM; 2.4. When to Learn; 2.5. Summary; 3. Similarity-Based Learning in OCCAM; 3.1. Similarity-based learning and memory organization; 3.1.1. IPP; 3.1.2. CYRUS; 3.1.3. Empirical learning; 3.1.4. Empirical learning techniques; 3.2. Aggregation; 3.2.1. Classifying a new event. 3.2.2. Finding similar events3.2.3. Finding similar events: An example; 3.3. Constructing a General Description of a Cluster of Events; 3.3.1. Finding common features; 3.3.2. Finding common features: An example; 3.3.3. Creating a new schema; 3.3.4. Intentional links; 3.3.5. Creating a macro-schema: An example; 3.4. Refining Schemata; 3.4.1. Refining a schema: An example; 3.5. Summary; 4.1. A Theory of Causality; 4.1.1. Constraints on causal relationships; 4.2. The Role of General Theories of Causality; 4.2.1. The effect of different tasks. 4.2.2. Selecting a cause consistent with a general theory of causality4.2.3. Knowledge of a causal mechanism; 4.3. Representing a Theory of Causality; 4.4. The Process of Theory-driven Learning; 4.4.1. Evaluation of schemata created by theory-driven learning; 4.4.2. Theory-driven learning: An example; 4.5. Dispositions; 4.5.1. Dispositions: An example; 4.5.2. Refining a dispositional attribute; 4.6. Experimental Results: TDL and SBL; 4.7. Learning Social Theories; 4.8. Summary; 4. Theory-Driven Learning in OCCAM; 5. Explanation-Based Learning in OCCAM.
Responsibility: Michael J. Pazzani.
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Abstract:

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.

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