Persistent Mappings in Cross-Domain Analogical Learning of Physics Domains

Abstract

Cross-domain analogies are a powerful method for learning new domains. This paper extends the Domain Transfer via Analogy (DTA) method with the idea of persistent mappings, correspondences between domains that are incrementally built up as a system gains experience with a new domain. We evaluate DTA plus persistent mappings by learning three domains (rotational mechanics, electricity, and heat) by analogy with linear mechanics, showing that persistent mappings improves performance.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2009
Accession Number
ADA501855

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  • Ken Forbus
  • Matthew Klenk

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  • Energy and Power Technologies

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  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Capacitance
  • Cognitive Science
  • Computer Science
  • Cross Domain
  • Electricity
  • Energy
  • Equations
  • Kinetic Energy
  • Mechanics
  • Momentum
  • Physics
  • Psychology
  • Reasoning
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