25 citations · 26 across the 12 of their papers we have counts for
4 papers · 1 filter
Decomposed Inductive Procedure Learning: Learning Academic Tasks with Human-Like Data Efficiency
Daniel Weitekamp, Christopher MacLellan, Erik Harpstead +1
Human learning relies on specialization -- distinct cognitive mechanisms working together to enable rapid learning. In contrast, most modern neural networks rely on a single mechan…
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
Liang Zhang, Jionghao Lin, John Sabatini +6
Learning performance data describe correct and incorrect answers or problem-solving attempts in adaptive learning, such as in intelligent tutoring systems (ITSs). Learning performa…
STAND: Self-Aware Precondition Induction for Interactive Task Learning
Daniel Weitekamp, Glen Smith, Kenneth Koedinger +1
In interactive task learning (ITL), AI agents learn new capabilities from limited human instruction provided during task execution. STAND is a new method of data-efficient rule pre…
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…