A Logical Theory of Robot Localization

V. Belle, H. J. Levesque

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

Abstract / Description of output

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 probabilistic 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 publicationAAAI Spring Symposium: Knowledge Representation and Reasoning in Robotics
Number of pages8
Publication statusPublished - 2014
EventKnowledge Representation and Reasoning in Robotics: Symposium at AAAI Spring Symposium Series 2014 - Stanford, United States
Duration: 24 Mar 201426 Mar 2014


SymposiumKnowledge Representation and Reasoning in Robotics
Country/TerritoryUnited States
Internet address


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