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- W4301435598 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." @default.
- W4301435598 created "2022-10-05" @default.
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- W4301435598 date "2022-08-04" @default.
- W4301435598 modified "2023-10-18" @default.
- W4301435598 title "Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind" @default.
- W4301435598 doi "https://doi.org/10.48550/arxiv.2208.02914" @default.
- W4301435598 hasPublicationYear "2022" @default.
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