Journal ArticleDOI
Mind Games: Game Engines as an Architecture for Intuitive Physics
Reads0
Chats0
TLDR
The hypothesis that many intuitive physical inferences are based on a mental physics engine that is analogous in many ways to the machine physics engines used in building interactive video games is explored.About:
This article is published in Trends in Cognitive Sciences.The article was published on 2017-09-01. It has received 137 citations till now. The article focuses on the topics: Game physics & Physics engine.read more
Citations
More filters
Posted Content
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia,Jessica B. Hamrick,Victor Bapst,Alvaro Sanchez-Gonzalez,Vinicius Zambaldi,Mateusz Malinowski,Andrea Tacchetti,David Raposo,Adam Santoro,Ryan Faulkner,Caglar Gulcehre,H. Francis Song,Andrew J. Ballard,Justin Gilmer,George E. Dahl,Ashish Vaswani,Kelsey R. Allen,Charlie Nash,Victoria Langston,Chris Dyer,Nicolas Heess,Daan Wierstra,Pushmeet Kohli,Matthew Botvinick,Oriol Vinyals,Yujia Li,Razvan Pascanu +26 more
TL;DR: It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.
Journal ArticleDOI
Cognitive computational neuroscience
TL;DR: The authors review recent work at the intersection of cognitive science, computational neuroscience and artificial intelligence that develops and tests computational models mimicking neural and cognitive function during a wide range of tasks.
Journal ArticleDOI
See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion
TL;DR: This work proposes a methodology to emulate hierarchical reasoning and multisensory fusion in a robot that learns to play Jenga, a complex game that requires physical interaction to be played effectively.
Journal ArticleDOI
Ten-month-old infants infer the value of goals from the costs of actions
Shari Liu,Shari Liu,Tomer Ullman,Tomer Ullman,Tomer Ullman,Joshua B. Tenenbaum,Joshua B. Tenenbaum,Elizabeth S. Spelke,Elizabeth S. Spelke +8 more
TL;DR: Infants’ expectations were modeled as Bayesian inferences over utility-theoretic calculations, providing a bridge to recent quantitative accounts of action understanding in older children and adults.
Posted Content
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
TL;DR: This work presents a novel method that learns to discover objects and model their physical interactions from raw visual images in a purely unsupervised fashion and incorporates prior knowledge about the compositional nature of human perception to factor interactions between object-pairs and learn efficiently.
References
More filters
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI
Separate visual pathways for perception and action.
TL;DR: It is proposed that the ventral stream of projections from the striate cortex to the inferotemporal cortex plays the major role in the perceptual identification of objects, while the dorsal stream projecting from the stripping to the posterior parietal region mediates the required sensorimotor transformations for visually guided actions directed at such objects.
Journal ArticleDOI
Building machines that learn and think like people.
TL;DR: In this article, a review of recent progress in cognitive science suggests that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it.