TRESTLE: A Model of Concept Formation in Structured Domains
This work addresses the challenge of incremental concept learning with mixed data for cognitive science and AI, though it appears incremental as it builds on existing models.
The authors tackled the problem of modeling human concept formation across diverse attribute types and learning settings by introducing TRESTLE, a probabilistic incremental model that unifies prior approaches. They found that TRESTLE is competitive with nonincremental models and more closely approximates human behavior in supervised and unsupervised tasks.
The literature on concept formation has demonstrated that humans are capable of learning concepts incrementally, with a variety of attribute types, and in both supervised and unsupervised settings. Many models of concept formation focus on a subset of these characteristics, but none account for all of them. In this paper, we present TRESTLE, an incremental account of probabilistic concept formation in structured domains that unifies prior concept learning models. TRESTLE works by creating a hierarchical categorization tree that can be used to predict missing attribute values and cluster sets of examples into conceptually meaningful groups. It updates its knowledge by partially matching novel structures and sorting them into its categorization tree. Finally, the system supports mixed-data representations, including nominal, numeric, relational, and component attributes. We evaluate TRESTLE's performance on a supervised learning task and an unsupervised clustering task. For both tasks, we compare it to a nonincremental model and to human participants. We find that this new categorization model is competitive with the nonincremental approach and more closely approximates human behavior on both tasks. These results serve as an initial demonstration of TRESTLE's capabilities and show that, by taking key characteristics of human learning into account, it can better model behavior than approaches that ignore them.