Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Reasoning about Continuous Uncertainty in the Situation Calculus ∗

Citations in the corpus

• Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, background): “In [Belle and Levesque, 2013a], we showed how this limitation could be lifted.”
• Reasoning about Probabilities in Unbounded First-Order Dynamical Domains (2017, background): “Our sense is that earlier results for the BHL scheme [Belle and Levesque, 2013b; 2014] can be reformulated forDS with some effort.”
• A Logical Theory of Localization (2015, methodology): “This limitation has been recently lifted in [5].”
• A logical theory of robot localization (2014, methodology): “This limitation has been recently lifted in [4], which we briefly review below and is based on two distinguished binary fluents l and p.”
• Reasoning about Probabilities in Dynamic Systems using Goal Regression (2013, background): “For discussions on these and non-dynamic proposals such as Markov logics (Richardson and Domingos, 2006), see (Belle and Levesque, 2013a,b).”
• How to Progress Beliefs in Continuous Domains (2014, background): “BL deal with this issue us-ing regression : beliefs are regressed to the initial state where (P3) does hold, and all the actual belief calculations can be done in the initial state (Belle and Levesque 2013b).”
• ALLEGRO: Belief-Based Programming in Stochastic Dynamical Domains (2015, methodology): “To remedy this, we [Belle and Levesque, 2013a] proposed a logical account for reasoning about probabilistic degrees of ∗Partially funded by the FWO project on Data Cleaning and KU Leuven’s GOA on Declarative Modeling for Mining and Learning. belief against noise in a continuous setting, building on…”
• PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains (2014, methodology): “See (Bacchus, Halpern, and Levesque 1999; Belle and Levesque 2013a) for more discussions.”