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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