Learning concepts from sketches via analogical generalization and near-misses

Modeling how concepts are learned from experience is an important challenge for cognitive science. In cognitive psychology, progressive alignment, i.e., comparing highly similar examples, has been shown to lead to rapid learning. In AI, providing negative examples (near-misses) that are very similar has been proposed as another way to accelerate learning. This paper describes a model of concept learning that combines these two ideas, using sketched input as a means of automatically encoding data to reduce tailorability. SEQL, which models analogical generalization, is used to implement progressive alignment. The processing of nearmiss examples is modeled by using the Structure Mapping Engine to hypothesize classification criteria based on differences. This near-miss analysis is performed both on labeled negative examples provided as input, and by using analogical retrieval to find near-miss examples when positive examples are provided. We use a corpus of sketches to show that the model can learn concepts based on sketches and that incorporating near-miss analysis improves learning.