1.6k citations · 9.7k across the 222 of their papers we have counts for
10 papers · 2 filters
At Human Speed: Deep Reinforcement Learning with Action Delay
Vlad Firoiu, Tina Ju, Josh Tenenbaum
There has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of tasks, from video games to motor control to board games, are now solv…
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Kexin Yi, Jiajun Wu, Chuang Gan +3
We marry two powerful ideas: deep representation learning for visual recognition and language understanding, and symbolic program execution for reasoning. Our neural-symbolic visua…
Logical Rule Induction and Theory Learning Using Neural Theorem Proving
Andres Campero, Aldo Pareja, Tim Klinger +2
A hallmark of human cognition is the ability to continually acquire and distill observations of the world into meaningful, predictive theories. In this paper we present a new mecha…
Modeling human intuitions about liquid flow with particle-based simulation
Christopher J. Bates, Ilker Yildirim, Joshua B. Tenenbaum +1
Humans can easily describe, imagine, and, crucially, predict a wide variety of behaviors of liquids--splashing, squirting, gushing, sloshing, soaking, dripping, draining, trickling…
Propagation Networks for Model-Based Control Under Partial Observation
Yunzhu Li, Jiajun Wu, Jun-Yan Zhu +3
There has been an increasing interest in learning dynamics simulators for model-based control. Compared with off-the-shelf physics engines, a learnable simulator can quickly adapt…
Learning to Share and Hide Intentions using Information Regularization
DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum +2
Learning to cooperate with friends and compete with foes is a key component of multi-agent reinforcement learning. Typically to do so, one requires access to either a model of or i…
Flexible Neural Representation for Physics Prediction
Damian Mrowca, Chengxu Zhuang, Elias Wang +4
Humans have a remarkable capacity to understand the physical dynamics of objects in their environment, flexibly capturing complex structures and interactions at multiple levels of…
Blaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation
Edmond Awad, Sydney Levine, Max Kleiman-Weiner +5
When a semi-autonomous car crashes and harms someone, how are blame and causal responsibility distributed across the human and machine drivers? In this article, we consider cases i…
The Three Pillars of Machine Programming
Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul +6
In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention…
A Computational Model of Commonsense Moral Decision Making
Richard Kim, Max Kleiman-Weiner, Andres Abeliuk +4
We introduce a new computational model of moral decision making, drawing on a recent theory of commonsense moral learning via social dynamics. Our model describes moral dilemmas as…