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  1. Regression and progression in stochastic domains.Vaishak Belle & Hector J. Levesque - 2020 - Artificial Intelligence 281 (C):103247.
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  • Planning and acting in partially observable stochastic domains.Leslie Pack Kaelbling, Michael L. Littman & Anthony R. Cassandra - 1998 - Artificial Intelligence 101 (1-2):99-134.
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  • (1 other version)The logical foundations of goal-regression planning in autonomous agents.John L. Pollock - 1998 - Artificial Intelligence 106 (2):267-334.
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  • Stochastic dynamic programming with factored representations.Craig Boutilier, Richard Dearden & Moisés Goldszmidt - 2000 - Artificial Intelligence 121 (1-2):49-107.
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  • Advanced SMT techniques for weighted model integration.Paolo Morettin, Andrea Passerini & Roberto Sebastiani - 2019 - Artificial Intelligence 275 (C):1-27.
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  • A logic programming approach to knowledge-state planning, II: The system.Thomas Eiter, Wolfgang Faber, Nicola Leone, Gerald Pfeifer & Axel Polleres - 2003 - Artificial Intelligence 144 (1-2):157-211.
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  • (1 other version)Reasoning defeasibly about probabilities.John L. Pollock - 2011 - Synthese 181 (2):317-352.
    In concrete applications of probability, statistical investigation gives us knowledge of some probabilities, but we generally want to know many others that are not directly revealed by our data. For instance, we may know prob(P/Q) (the probability of P given Q) and prob(P/R), but what we really want is prob(P/Q& R), and we may not have the data required to assess that directly. The probability calculus is of no help here. Given prob(P/Q) and prob(P/R), it is consistent with the probability (...)
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  • A dynamic epistemic framework for reasoning about conformant probabilistic plans.Yanjun Li, Barteld Kooi & Yanjing Wang - 2019 - Artificial Intelligence 268 (C):54-84.
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  • Contingent planning under uncertainty via stochastic satisfiability.Stephen M. Majercik & Michael L. Littman - 2003 - Artificial Intelligence 147 (1-2):119-162.
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  • Convention in joint activity.Richard Alterman & Andrew Garland - 2001 - Cognitive Science 25 (4):611-657.
    Conventional behaviors develop from practice for regularly occurring problems of coordination within a community of actors. Reusing and extending conventional methods for coordinating behavior is the task of everyday reasoning.The computational model presented in the paper details the emergence of convention in circumstances where there is no ruling body of knowledge developed by prior generations of actors within the community to guide behavior. The framework we assume combines social theories of cognition with human information processing models that have been developed (...)
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  • A logic of time, chance, and action for representing plans.Peter Haddawy - 1996 - Artificial Intelligence 80 (2):243-308.
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  • Reasoning about discrete and continuous noisy sensors and effectors in dynamical systems.Vaishak Belle & Hector J. Levesque - 2018 - Artificial Intelligence 262 (C):189-221.
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  • Strong planning under partial observability.Piergiorgio Bertoli, Alessandro Cimatti, Marco Roveri & Paolo Traverso - 2006 - Artificial Intelligence 170 (4-5):337-384.
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  • A Logical Theory of Localization.Vaishak Belle & Hector J. Levesque - 2016 - Studia Logica 104 (4):741-772.
    A central problem in applying logical knowledge representation formalisms to traditional robotics is that the treatment of belief change is categorical in the former, while probabilistic in the latter. A typical example is the fundamental capability of localization where a robot uses its noisy sensors to situate itself in a dynamic world. Domain designers are then left with the rather unfortunate task of abstracting probabilistic sensors in terms of categorical ones, or more drastically, completely abandoning the inner workings of sensors (...)
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  • On the undecidability of probabilistic planning and related stochastic optimization problems.Omid Madani, Steve Hanks & Anne Condon - 2003 - Artificial Intelligence 147 (1-2):5-34.
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  • Equivalence notions and model minimization in Markov decision processes.Robert Givan, Thomas Dean & Matthew Greig - 2003 - Artificial Intelligence 147 (1-2):163-223.
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  • Planning under time constraints in stochastic domains.Thomas Dean, Leslie Pack Kaelbling, Jak Kirman & Ann Nicholson - 1995 - Artificial Intelligence 76 (1-2):35-74.
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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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