@article {5065,
	title = { AGENT: A Benchmark for Core Psychological Reasoning},
	year = {2021},
	month = {07/2021},
	author = {Tianmin Shu and Abhishek Bhandwaldar and Chuang Gan and Kevin A Smith and Shari Liu and Dan Gutfreund and Elizabeth S Spelke and Joshua B. Tenenbaum and Tomer D. Ullman}
}
@conference {4817,
	title = {The fine structure of surprise in intuitive physics: when, why, and how much?},
	booktitle = {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},
	year = {2020},
	url = {https://cogsci.mindmodeling.org/2020/papers/0761/index.html},
	author = {Kevin A Smith and Lingjie Mei and Shunyu Yao and Jiajun Wu and Elizabeth S Spelke and Joshua B. Tenenbaum and Tomer D. Ullman},
	editor = {Stephanie Denison and Michael Mack and Yang Xu and Blair C. Armstrong}
}
@proceedings {4380,
	title = {Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations},
	year = {2019},
	month = {11/2019},
	address = {Vancouver, Canada},
	abstract = {<p>From infancy, humans have expectations about how objects will move and interact. Even young children expect objects not to move through one another, teleport, or disappear. They are surprised by mismatches between physical expectations and perceptual observations, even in unfamiliar scenes with completely novel objects. A model that exhibits human-like understanding of physics should be similarly surprised, and adjust its beliefs accordingly. We propose ADEPT, a model that uses a coarse (approximate geometry) object-centric representation for dynamic 3D scene understanding. Inference integrates deep recognition networks, extended probabilistic physical simulation, and particle filtering for forming predictions and expectations across occlusion. We also present a new test set for measuring violations of physical expectations, using a range of scenarios derived from de- velopmental psychology. We systematically compare ADEPT, baseline models, and human expectations on this test set. ADEPT outperforms standard network architectures in discriminating physically implausible scenes, and often performs this discrimination at the same level as people.</p>
},
	url = {http: //physadept.csail.mit.edu/},
	author = {Kevin A Smith and Lingjie Mei and Shunyu Yao and Jiajun Wu and Elizabeth S Spelke and Joshua B. Tenenbaum and Tomer D. Ullman}
}
