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Learning Modeling Abstractions via Generalization

In domains with everyday scenarios, an important aspect of model formulation concerns moving from broad descriptions to the technical abstractions necessary for effective problem-solving. We present a method for learning how to make abstraction decisions from experience via analogical generalization. Specifically, we generalize abstraction decisions from worked examples, abstracting away irrelevant information. When faced with a new situation, our method compares the entities in the situation with the generalizations, and makes its decision by using the best match. We argue that the similarity score from the comparison is an effective heuristic for judging the quality of the modeling decision. Using textbook physics problems, we show that our method can make accurate abstraction decisions, and that these decisions improve as the system gains experience.