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Grounding Mundane Inference in Perception

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Abstract

We describe a uniform technique for representing both sensory data and the attentional state of an agent using a subset of modal logic with indexicals. The resulting representation maps naturally into feed-forward parallel networks or can be implemented on stock hardware using bit-mask instructions. The representation has “circuit-semantics” (Nilsson, 1994, Rosenschein and Kaelbling, 1986), but can efficiently represent propositions containing modals, unary predicates, and functions. We describe an example using Kludge, a vision-based mobile robot programmed to perform simple natural language instructions involving fetching and following tasks.

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Authors and Affiliations

  1. The Institute for the Learning Sciences, Northwestern University, 1890 Maple Avenue, Evanston, IL, 60201

    Ian Douglas Horswill

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Horswill, I.D. Grounding Mundane Inference in Perception. Autonomous Robots 5, 63–77 (1998). https://doi.org/10.1023/A:1008865025943

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  • DOI: https://doi.org/10.1023/A:1008865025943