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arXiv · 2208.02914

Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind

Tan Zhi-Xuan·Nishad Gothoskar·Falk Pollok·Dan Gutfreund·Joshua B. Tenenbaum·Vikash K. Mansinghka
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Abstract

To facilitate the development of new models to bridge the gap between machine and human social intelligence, the recently proposed Baby Intuitions Benchmark (arXiv:2102.11938) provides a suite of tasks designed to evaluate commonsense reasoning about agents' goals and actions that even young infants exhibit. Here we present a principled Bayesian solution to this benchmark, based on a hierarchically Bayesian Theory of Mind (HBToM). By including hierarchical priors on agent goals and dispositions, inference over our HBToM model enables few-shot learning of the efficiency and preferences of an agent, which can then be used in commonsense plausibility judgements about subsequent agent behavior. This approach achieves near-perfect accuracy on most benchmark tasks, outperforming deep learning and imitation learning baselines while producing interpretable human-like inferences, demonstrating the advantages of structured Bayesian models of human social cognition.

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Tan Zhi-Xuan, Nishad Gothoskar, Falk Pollok, Dan Gutfreund, Joshua B. Tenenbaum, Vikash K. Mansinghka. 2022-08-04. Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind. https://arxiv.org/abs/2208.02914

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