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