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Filters: Author is Kevin A. Smith [Clear All Filters]
An approximate representation of objects underlies physical reasoning. psyArXiv (2022). at <https://psyarxiv.com/vebu5/>
AGENT: A Benchmark for Core Psychological Reasoning. Proceedings of the 38th International Conference on Machine Learning (2021).
Meta-strategy learning in physical problem solving: the effect of embodied experience. bioRxiv (2021).
Partial Mental Simulation Explains Fallacies in Physical Reasoning. psyArXiv (2021). at <https://psyarxiv.com/y4a8x>
The fine structure of surprise in intuitive physics: when, why, and how much?. Proceedings of the 42th Annual Meeting of the Cognitive Science Society - Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, virtual, July 29 - August 1, 2020 ( ) (2020). at <https://cogsci.mindmodeling.org/2020/papers/0761/index.html>
Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning. Proceedings of the National Academy of Sciences 201912341 (2020). doi:10.1073/pnas.1912341117
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019). at <http: //physadept.csail.mit.edu/>
Differentiable physics and stable modes for tool-use and manipulation planning. Robotics: Science and Systems 2018 (2018).
End-to-end differentiable physics for learning and control. Advances in Neural Information Processing Systems 31 (NIPS 2018) (2018).
Faulty Towers: A counterfactual simulation model of physical support. Proceedings of the 39th Annual Conference of the Cognitive Science Society (2017).
Rapid Physical Predictions from Convolutional Neural Networks. Neural Information Processing Systems, Intuitive Physics Workshop (2016). at <http://phys.csail.mit.edu/papers/9.pdf>