Abstract knowledge guides search and prediction in novel situations


People combine their abstract knowledge about the world with data they have gathered in order to guide search and prediction in everyday life. We present a Bayesian model that formalizes knowledge transfer. Our model consists of two components: a hierarchical Bayesian model of learning and a Markov Decision Process modeling planning and search. An experiment tests qualitative predictions of the model, showing a strong fit between human data and model predictions. We conclude by discussing relations to previous work and future directions.

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