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Goal Recognition is the task of inferring the goal of an agent from their action logs. Goal Recognition assumes these logs are collected by an independent process that is not controlled by
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Bayesian deep learning (e.g., Monte Carlo dropout, deep ensembles, Laplace approximations, and variational inference), several challenges remain: Scalability: Many Bayesian inference methods
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networks, Bayesian inference, computational neuroscience, mathematics.
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that generates explanatory theories with a question-answering inference machine, and operating with explicit notions of uncertainty so as to mitigate overconfident predictions [1]. A complementary line
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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for inference, yet differs from standard Bayesian approaches through its information-theoretic foundation. The MML87 approximation achieves computational tractability while remaining virtually identical to Strict
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polycrystalline material during plastic deformation in order to eventually predict the manner in which materials deform and fail. As a first step, we wish to infer a distribution of the directions of deformation