Artificial Intelligence - Special volume on qualitative reasoning about physical systems
The structure-mapping engine: algorithm and examples
Artificial Intelligence
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Building problem solvers
Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
Artificial Intelligence
Automated modeling for answering prediction questions: selecting the time scale and system boundary
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Analogy in Inductive Theorem Proving
Journal of Automated Reasoning
Companion cognitive systems: a step toward human-level AI
AI Magazine - Special issue on achieving human-level AI through integrated systems and research
Value-function-based transfer for reinforcement learning using structure mapping
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Strategy variations in analogical problem solving
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Solving everyday physical reasoning problems by analogy using sketches
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Transfer learning in real-time strategy games using hybrid CBR/RL
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Case-Based Reasoning in Transfer Learning
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Analogical model formulation for transfer learning in AP Physics
Artificial Intelligence
Domain transfer via cross-domain analogy
Cognitive Systems Research
Exploiting persistent mappings in cross-domain analogical learning of physical domains
Artificial Intelligence
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Transfer learning is the ability of an agent to apply knowledge learned in previous tasks to new problems or domains. We approach this problem by focusing on model formulation, i.e., how to move from the unruly, broad set of concepts used in everyday life to a concise, formal vocabulary of abstractions that can be used effectively for problem solving. This paper describes how the Companions cognitive architecture uses analogical model formulation to learn to solve AP Physics problems. Our system starts with some basic mathematical skills, a broad common sense ontology, and some qualitative mechanics, but no equations. Our system uses worked solutions to learn how to use equations and modeling assumptions to solve AP Physics problems. We show that this process of analogical model formulation can facilitate learning over a range of types of transfer, in an experiment administered by the Educational Testing Service.