Reasoning about Noisy Sensors and Eectors in the Situation Calculus

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Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, background): “…ﬁrst-order logic and probability [Bacchus, 1990], perhaps the most general formalism for dealing with degrees of belief in formulas, and in particular, with how belief should evolve in the presence of noisy sensing and acting is the account proposed by Bacchus, Halpern, and Levesque (BHL) [1999].”
A logical theory of robot localization (2014, background): “9 will not imply full confidence in v being in [1, 2]∪ [3, 4]∪ [7, 8].”
A First-Order Logic of Probability and Only Knowing in Unbounded Domains (2016, background): “Similarly, in a robotic setting (Bacchus, Halpern, and Levesque 1999), some outcomes are more likely than others, perhaps due to error prone actuators.”
A Logical Theory of Localization (2015, background): “However, if v 1 but the robot’s orientation is such that the sonar’s signals advance through the gap at [1 , 2], then the robot’s sonar unit would suggest values closer to δ + λ rather than δ alone.”