A logical theory of robot localization

By Vaishak Belle, Hector J. Levesque. 2014

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.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Vaishak Belle
2. Hector J. Levesque
3. Giulia Vilone
4. Timo Speith

Citations in the corpus

• Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, background): “In [Belle and Levesque, 2014b; 2015b], we developed a logical theory where a number of standard and non-standard variations pertaining to localization could be tested.”