How to Progress Beliefs in Continuous Domains

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Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, background): “Nonetheless, given queries about outcomes after actions, as needed for automated planning, we have shown that actions can be either “compiled” away, or “injected” to the current state [Belle and Levesque, 2013b; 2014a].”
A Logical Theory of Localization (2015, background): “and ψ denotes (v + tan θ ∈ [1, 2]) ∨ (v + tan θ ∈ [3, 4]) ∨ (v + tan θ ∈ [7, 8]).”
ALLEGRO: Belief-Based Programming in Stochastic Dynamical Domains (2015, background): “In a more recent paper [Belle and Levesque, 2014b], we then presented a system called prego that is able to effectively calculate the degree of belief in φ as a function of the initial beliefs of the robot, the noisy physical actions that have taken place, and the noisy sensing results that have…”
PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains (2014, methodology): “Following (Belle and Levesque 2014), we would like to explore progression in prego and perhaps evaluate that against particle filters (Thrun, Burgard, and Fox 2005).”
Reasoning about Probabilities in Unbounded First-Order Dynamical Domains (2017, result): “Our sense is that earlier results for the BHL scheme [Belle and Levesque, 2013b; 2014] can be reformulated forDS with some effort.”