Learning to Predict and Explain: An Integration of Similarity-Based,Theory-Driven,and Explanation-Based Learning |
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| Abstract: | We analyze the types of information that human learners rely on in the acquisition of predictive and explanatory knowledge. We present OCCAM, a computational model of this learning task that integrates three learning methods: similarity-based learning (SBL), explanation-based learning (EBL), and theory-driven learning (TDL). We focus on the strengths and weaknesses of each learning method and describe how they can be integrated in a complementary fashion. The goal of this integration is to provide a learning architecture that accounts for the effects of prior knowledge on human learning. The integration helps to explain how a learner can learn rapidly when new experiences are consistent with prior knowledge while still retaining the ability to learn in novel domains (although more slowly). In addition, we present experimental evidence that an integrated model converges on accurate concepts more rapidly than either method applied individually. OCCAM is unique among learning models in that it can make use of data-intensive learning methods to acquire the knowledge needed for knowledge-intensive learning. |
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