Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
TINLAP '75 Proceedings of the 1975 workshop on Theoretical issues in natural language processing
Eleven proofs of sin2x+cos2x = 1
ACM SIGSAM Bulletin
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An artificial intelligence system that learns by observing its users perform symbolic mathematical problem solving is presented. This fully-implemented system is being evaluated as a problem solver in the domain of classical physics. Using its mathematical and physical knowledge, the system determines why a human-provided solution to a specific problem suffices to solve the problem, and then extends the solution technique to more general situations, thereby improving its own problem-solving performance. This research illustrates a need for symbolic mathematics systems to produce explanations of their problem-solving steps, as these explanations guide learning. Although physics problem solving is currently being investigated, the results obtained are relevant to other mathematically-based domains. This work also has implications for intelligent computer-aided instruction in domains of this type.