A Logical Theory of Robot Localization

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.