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

• URL: http://arxiv.org/abs/2110.12301v1
• Date: Sat, 23 Oct 2021 21:23:04 GMT
• Title: Map Induction: Compositional spatial submap learning for efficient exploration in novel environments
• Authors: Sugandha Sharma, Aidan Curtis, Marta Kryven, Josh Tenenbaum, Ila Fiete
• Abstract summary: We show that humans explore new environments efficiently by inferring the structure of unobserved spaces. Using a new behavioral Map Induction Task, we demonstrate that this computational framework explains human exploration behavior better than non-inductive models.
• Score: 25.00757828975447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans are expert explorers. Understanding the computational cognitive mechanisms that support this efficiency can advance the study of the human mind and enable more efficient exploration algorithms. We hypothesize that humans explore new environments efficiently by inferring the structure of unobserved spaces using spatial information collected from previously explored spaces. This cognitive process can be modeled computationally using program induction in a Hierarchical Bayesian framework that explicitly reasons about uncertainty with strong spatial priors. Using a new behavioral Map Induction Task, we demonstrate that this computational framework explains human exploration behavior better than non-inductive models and outperforms state-of-the-art planning algorithms when applied to a realistic spatial navigation domain.

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