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Efficient Partial Simulation Quantitatively Explains Deviations from Optimal Physical Predictions
Ilona Bass · Kevin Smith · Elizabeth Bonawitz · Tomer Ullman

Tue Dec 14 11:04 AM -- 11:06 AM (PST) @
in Physical Reasoning and Inductive Biases for the Real World »

Humans are adept at planning actions in real-time dynamic physical environments. Machine intelligence struggles with this task, and one cause is that running simulators of complex, real-world environments is computationally expensive. Yet recent accounts suggest that humans use mental simulation in order to make intuitive physical judgments. How is human physical reasoning so accurate, while maintaining computational tractability? We suggest that human behavior is well described by partial simulation, which moves forward in time only parts of the world deemed relevant. We take as a case study Ludwin-Peery, Bramley, Davis, and Gureckis (2020), in which a conjunction fallacy was found in the domain of intuitive physics. This phenomenon is difficult to explain with full simulation, but we show it can be quantitatively accounted for with partial simulation. We discuss how AI research could make use of efficient partial simulation in implementations of commonsense physical reasoning.

Author Information

Ilona Bass (Harvard University)
Kevin Smith (MIT)
Elizabeth Bonawitz (Rutgers University - Newark)
Tomer Ullman (Harvard)

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