6 citations · 11 across the 7 of their papers we have counts for
8 papers
TRESTLE: A Model of Concept Formation in Structured Domains
Christopher J. MacLellan, Erik Harpstead, Vincent Aleven +1
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 unsup…
Evaluating Alternative Training Interventions Using Personalized Computational Models of Learning
Christopher James MacLellan, Kimberly Stowers, Lisa Brady
Evaluating different training interventions to determine which produce the best learning outcomes is one of the main challenges faced by instructional designers. Typically, these d…
Interactive Learning of Hierarchical Tasks from Dialog with GPT
Lane Lawley, Christopher J. MacLellan
We present a system for interpretable, symbolic, interactive task learning from dialog using a GPT model as a conversational front-end. The learned tasks are represented as hierarc…
Efficient Induction of Language Models Via Probabilistic Concept Formation
Christopher J. MacLellan, Peter Matsakis, Pat Langley
This paper presents a novel approach to the acquisition of language models from corpora. The framework builds on Cobweb, an early system for constructing taxonomic hierarchies of p…
Do it Like the Doctor: How We Can Design a Model That Uses Domain Knowledge to Diagnose Pneumothorax
Glen Smith, Qiao Zhang, Christopher MacLellan
Computer-aided diagnosis for medical imaging is a well-studied field that aims to provide real-time decision support systems for physicians. These systems attempt to detect and dia…
Convolutional Cobweb: A Model of Incremental Learning from 2D Images
Christopher J. MacLellan, Harshil Thakur
This paper presents a new concept formation approach that supports the ability to incrementally learn and predict labels for visual images. This work integrates the idea of convolu…
Decomposed Inductive Procedure Learning
Daniel Weitekamp, Christopher MacLellan, Erik Harpstead +1
Recent advances in machine learning have made it possible to train artificially intelligent agents that perform with super-human accuracy on a great diversity of complex tasks. How…
Learning Cognitive Models using Neural Networks
Devendra Singh Chaplot, Christopher MacLellan, Ruslan Salakhutdinov +1
A cognitive model of human learning provides information about skills a learner must acquire to perform accurately in a task domain. Cognitive models of learning are not only of sc…