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

Download Creating A Memory of Causal Relationships: An Integration of Empirical and Explanation-based Learning Methods fb2

by Michael J. Pazzani

  • ISBN: 0805806296
  • Category: Technology
  • Author: Michael J. Pazzani
  • Subcategory: Computer Science
  • Other formats: azw lrf lrf lit
  • Language: English
  • Publisher: Psychology Press; 1 edition (May 1, 1990)
  • Pages: 360 pages
  • FB2 size: 1718 kb
  • EPUB size: 1542 kb
  • Rating: 4.9
  • Votes: 509
Download Creating A Memory of Causal Relationships: An Integration of Empirical and Explanation-based Learning Methods fb2

Home Browse Books Book details, Creating a Memory of Causal Relationships: A. .

Home Browse Books Book details, Creating a Memory of Causal Relationships: A.Creating a Memory of Causal Relationships: An Integration of Empirical and Explanation-Based Learning Methods. By Michael J. Pazzani. 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.

Explanation-Based Learning in OCCAM. Integration of Learning Methods. Contents: Introduction. What OCCAM Is Up Against. Similarity-Based Learning in OCCAM. Experiments in Integrated Learning. Future Directions and Conclusions. Appendices: Data Listing. OCCAM's Generalization Rules. Listing of Economic Sanction Incidents. Theory-Driven Learning in OCCAM. Explanation-Based Learning in OCCAM.

Abductive explanation-based learning: A solution to the explanation problem. Creating a memory of causal relationships: An integrationof empirical and explanation-based learning methods. Cohen, W. (this issue). Dietterich, T. (1990). Hillsdale, NJ: LawrenceErlbaum. The influence of prior knowledge on concept acquisition:Experimental and computational results.

Creating a Memory of Causal Relationships : An Integration of Empirical and Explanation-Based Learning .

Creating a Memory of Causal Relationships : An Integration of Empirical and Explanation-Based Learning Methods.

This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of. Experiments in Integrated Learning

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.

OCCAM uses explanation-based methods, but does not address the utility . Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem.

OCCAM uses explanation-based methods, but does not address the utility problem. 1 Qualitative computational models of human learning. The problem addressed in Creating a Memory of Causal Relationships differs from that commonly studied in machine learning. It is designed to approximate the type of learning situation that young children and adults encounter when learning predictive relationships between actions and their consequences Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem.

An Integration of Empirical and Explanation-based Learning Methods. By: Michael J. Publisher: Psychology Press. Print ISBN: 9780805807899, 0805807896.

Causal relationships among a set of observed variables are often modeled .

Causal relationships among a set of observed variables are often modeled using directed acyclic graph (DAG) structures, and learning such structures from data is known as the causal discovery problem. We here consider the learning of linear non-Gaussian acyclic models with hidden variables  .

Creating a memory of causal relationships: An integration of empirical and explanation-based learning methods. Hillsdale, NJ: Lawrence Erlbaum Associates. Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann. Book Chapters 1. Pazzani, M. (1988). Explanation-based learning for knowledge-based systems. In B. Gaines & J. Boose (Ed., Knowledge acquisition for knowledge-based systems. London: Academic Press.

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.

Related to Creating A Memory of Causal Relationships: An Integration of Empirical and Explanation-based Learning Methods fb2 books: