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Humans are expert explorers. Understanding the computational cognitivemechanisms that support this efficiency can advance the study of the human mindand enable more efficient exploration algorithms. We hypothesize that humansexplore new environments efficiently by inferring the structure of unobservedspaces using spatial information collected from previously explored spaces.This cognitive process can be modeled computationally using program inductionin a Hierarchical Bayesian framework that explicitly reasons about uncertaintywith strong spatial priors. Using a new behavioral Map Induction Task, wedemonstrate that this computational framework explains human explorationbehavior better than non-inductive models and outperforms state-of-the-artplanning algorithms when applied to a realistic spatial navigation domain.

Map Induction: Compositional spatial submap learning for efficient exploration in novel environments