1.6k citations · 9.7k across the 222 of their papers we have counts for
6 papers · 2 filters
Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
Kelsey R. Allen, Kevin A. Smith, Joshua B. Tenenbaum
Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their cap…
Explaining intuitive difficulty judgments by modeling physical effort and risk
Ilker Yildirim, Basil Saeed, Grace Bennett-Pierre +3
The ability to estimate task difficulty is critical for many real-world decisions such as setting appropriate goals for ourselves or appreciating others' accomplishments. Here we g…
Few-Shot Bayesian Imitation Learning with Logical Program Policies
Tom Silver, Kelsey R. Allen, Alex K. Lew +2
Humans can learn many novel tasks from a very small number (1--5) of demonstrations, in stark contrast to the data requirements of nearly tabula rasa deep learning methods. We prop…
Learning to Infer Program Sketches
Maxwell Nye, Luke Hewitt, Joshua Tenenbaum +1
Our goal is to build systems which write code automatically from the kinds of specifications humans can most easily provide, such as examples and natural language instruction. The…
The Omniglot challenge: a 3-year progress report
Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum
Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks. The model was not mea…
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning
Michael Shum, Max Kleiman-Weiner, Michael L. Littman +1
Human social behavior is structured by relationships. We form teams, groups, tribes, and alliances at all scales of human life. These structures guide multi-agent cooperation and c…