Abstract
This paper descibes an explanation-based learning (EBL) system based on a version of Newell, Shaw, and Simon's LOGIC-THEORIST (LT). Results of applying this system to propositional calculus problems from Principia Mathematica are compared with results of applying several other versions of the same performance element to these problems. The primary goal of this study is to characterize and analyze differences between non-learning, rote learning (LT's original learning method), and EBL. Another aim is to provide a characterization of the performance of a simple problem solver in the context of the Principia problems, in the hope that these problems can be used as a benchmark for testing improved learning methods, just as problems like chess and the eight puzzle have been used as benchmarks in research on search methods.
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O'Rorke, P. LT Revisited: Explanation-Based Learning and the Logic of Principia Mathematica. Machine Learning 4, 117–159 (1989). https://doi.org/10.1023/A:1022647915955
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DOI: https://doi.org/10.1023/A:1022647915955