Reasoning about Discrete and Continuous Noisy Sensors and Effectors in Dynamical Systems
This work addresses a foundational problem in robotics and AI by enabling more expressive reasoning in complex noisy environments, though it is incremental as it builds on an existing approach.
The paper tackles the limitation of a logical formalism for reasoning about noisy sensing and acting, which was restricted to discrete domains, by extending it to handle both discrete and continuous domains within a unified framework.
Among the many approaches for reasoning about degrees of belief in the presence of noisy sensing and acting, the logical account proposed by Bacchus, Halpern, and Levesque is perhaps the most expressive. While their formalism is quite general, it is restricted to fluents whose values are drawn from discrete finite domains, as opposed to the continuous domains seen in many robotic applications. In this work, we show how this limitation in that approach can be lifted. By dealing seamlessly with both discrete distributions and continuous densities within a rich theory of action, we provide a very general logical specification of how belief should change after acting and sensing in complex noisy domains.