Artificial Intelligence - Special volume on qualitative reasoning about physical systems
The structure-mapping engine: algorithm and examples
Artificial Intelligence
A computational model of analogical problem solving
Similarity and analogical reasoning
Similarity and analogical reasoning
Building problem solvers
Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
Model-based reasoning about learner behaviour
Artificial Intelligence
Analogy in Inductive Theorem Proving
Journal of Automated Reasoning
Metaphors and heuristic-driven theory projection (HDTP)
Theoretical Computer Science - Algebraic methods in language processing
Learning from Inconsistencies in an Integrated Cognitive Architecture
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Analogy as Integrating Framework for Human-Level Reasoning
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Strategy variations in analogical problem solving
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Measuring the level of transfer learning by an AP physics problem-solver
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Achieving far transfer in an integrated cognitive architecture
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
ACM SIGKDD Explorations Newsletter
Exploiting persistent mappings in cross-domain analogical learning of physical domains
Artificial Intelligence
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Analogical learning has long been seen as a powerful way of extending the reach of one's knowledge. We present the domain transfer via analogy (DTA) method for learning new domain theories via cross-domain analogy. Our model uses analogies between pairs of textbook example problems, or worked solutions, to create a domain mapping between a familiar and a new domain. This mapping allows us to initialize a new domain theory. After this initialization, another analogy is made between the domain theories themselves, providing additional conjectures about the new domain. We present two experiments in which our model learns rotational kinematics by an analogy with translational kinematics, and vice versa. These learning rates outperform those from a version of the system that is incrementally given the correct domain theory.