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  1. Probabilistic dynamic epistemic logic.Barteld P. Kooi - 2003 - Journal of Logic, Language and Information 12 (4):381-408.
    In this paper I combine the dynamic epistemic logic ofGerbrandy (1999) with the probabilistic logic of Fagin and Halpern (1994). The resultis a new probabilistic dynamic epistemic logic, a logic for reasoning aboutprobability, information, and information change that takes higher orderinformation into account. Probabilistic epistemic models are defined, and away to build them for applications is given. Semantics and a proof systemis presented and a number of examples are discussed, including the MontyHall Dilemma.
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  • (1 other version)On the logic of theory change: Partial meet contraction and revision functions.Carlos E. Alchourrón, Peter Gärdenfors & David Makinson - 1985 - Journal of Symbolic Logic 50 (2):510-530.
    This paper extends earlier work by its authors on formal aspects of the processes of contracting a theory to eliminate a proposition and revising a theory to introduce a proposition. In the course of the earlier work, Gardenfors developed general postulates of a more or less equational nature for such processes, whilst Alchourron and Makinson studied the particular case of contraction functions that are maximal, in the sense of yielding a maximal subset of the theory (or alternatively, of one of (...)
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  • Reasoning about noisy sensors and effectors in the situation calculus.Fahiem Bacchus, Joseph Y. Halpern & Hector J. Levesque - 1999 - Artificial Intelligence 111 (1-2):171-208.
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  • Probabilities of conditionals and conditional probabilities.David Lewis - 1976 - Philosophical Review 85 (3):297-315.
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  • An analysis of first-order logics of probability.Joseph Y. Halpern - 1990 - Artificial Intelligence 46 (3):311-350.
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  • (1 other version)Some Philosophical Problems from the Standpoint of Artificial Intelligence.J. McCarthy & P. J. Hayes - 1969 - Machine Intelligence 4:463-502.
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  • Probabilistic logic.Nils J. Nilsson - 1986 - Artificial Intelligence 28 (1):71-87.
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  • Credal networks.Fabio G. Cozman - 2000 - Artificial Intelligence 120 (2):199-233.
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  • Robot location estimation in the situation calculus.Vaishak Belle & Hector J. Levesque - 2015 - Journal of Applied Logic 13 (4):397-413.
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  • Iterated belief change in the situation calculus.Steven Shapiro, Maurice Pagnucco, Yves Lespérance & Hector J. Levesque - 2011 - Artificial Intelligence 175 (1):165-192.
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  • Knowledge, action, and the frame problem.Richard B. Scherl & Hector J. Levesque - 2003 - Artificial Intelligence 144 (1-2):1-39.
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  • An algorithm for probabilistic planning.Nicholas Kushmerick, Steve Hanks & Daniel S. Weld - 1995 - Artificial Intelligence 76 (1-2):239-286.
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  • cc-Golog – An Action Language with Continuous Change.Henrik Grosskreutz & Gerhard Lakemeyer - 2003 - Logic Journal of the IGPL 11 (2):179-221.
    High-level robot controllers often need to specify event-driven behavior and they operate low-level processes which change the world in a continuous fashion. While non logic-based control languages have existed for some time to address these issues, this is not the case for logic-based languages. To remedy the situation, we show how to incorporate continuous change and event-driven behavior into the action language GOLOG. Besides proposing a suitable semantics for the new language, which we call cc-Golog, we show how a robot (...)
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