A Logical Theory of Robot Localization

Vaishak Belle, Hector Levesque

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A central problem in applying logical knowledge representation formalisms to traditional robotics is that the treatment of belief change is categorical in the former, while probabilistic in the latter. A typical example is the fundamental capability of localization where a robot uses its many noisy sensors to situate itself in a dynamic world. Domain designers are then left with the rather unfortunate task of abstracting probabilistic sensors in terms of categorical ones, or more drastically, completely abandoning the inner workings of sensors to black-box probabilistic tools and then interpreting their outputs in an abstract way. Building on a first-principles approach by Bacchus, Halpern and Levesque, and a recent continuous extension to it by Belle and Levesque, we provide an axiomatization that shows how localization can be realized as a basic action theory, thereby demonstrating how such capabilities can be enabled in a single logical framework.
Original languageEnglish
Title of host publicationProceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems
Place of PublicationRichland, SC
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems
Pages349-356
Number of pages8
ISBN (Print)978-1-4503-2738-1
Publication statusPublished - 2014

Publication series

NameAAMAS '14
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems

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  • A Logical Theory of Robot Localization

    Belle, V. & Levesque, H. J., 2014, AAAI Spring Symposium: Knowledge Representation and Reasoning in Robotics. 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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