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  1. Weak, strong, and strong cyclic planning via symbolic model checking.A. Cimatti, M. Pistore, M. Roveri & P. Traverso - 2003 - Artificial Intelligence 147 (1-2):35-84.
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  • Conformant planning via symbolic model checking and heuristic search.A. Cimatti, M. Roveri & P. Bertoli - 2004 - Artificial Intelligence 159 (1-2):127-206.
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  • SAT-based planning in complex domains: Concurrency, constraints and nondeterminism.Claudio Castellini, Enrico Giunchiglia & Armando Tacchella - 2003 - Artificial Intelligence 147 (1-2):85-117.
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  • Formalizing narratives using nested circumscription.Chitta Baral, Alfredo Gabaldon & Alessandro Provetti - 1998 - Artificial Intelligence 104 (1-2):107-164.
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  • An action language for multi-agent domains.Chitta Baral, Gregory Gelfond, Enrico Pontelli & Tran Cao Son - 2022 - Artificial Intelligence 302 (C):103601.
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  • Logic-based subsumption architecture.Eyal Amir & Pedrito Maynard-Zhang - 2004 - Artificial Intelligence 153 (1-2):167-237.
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  • Approximation of action theories and its application to conformant planning.Phan Huy Tu, Tran Cao Son, Michael Gelfond & A. Ricardo Morales - 2011 - Artificial Intelligence 175 (1):79-119.
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  • Reasoning about actions: steady versus stabilizing state constraints.Michael Thielscher - 1998 - Artificial Intelligence 104 (1-2):339-355.
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  • Reasoning about Sensing Actions in Domains with Multi-Valued Fluents.Tran Cao Son, Phan Huy Tu & Xin Zhang - 2005 - Studia Logica 79 (1):135-160.
    In this paper, we discuss the weakness of current action languages for sensing actions with respect to modeling domains with multi-valued fluents. To address this problem, we propose a language with sensing actions and multi-valued fluents, called AMK, provide a transition function based semantics for the language, and demonstrate its use through several examples from the literature. We then define the entailment relationship between action theories and queries in AMK, denoted by ⊧AMK, and discuss some properties about AMK.
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  • Formalizing sensing actions— A transition function based approach.Tran Cao Son & Chitta Baral - 2001 - Artificial Intelligence 125 (1-2):19-91.
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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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  • Metatheory of Actions: Beyond Consistency.Andreas Herzig & Ivan Varzinczak - 2007 - Artificial Intelligence 171 (1):951–984.
    Traditionally, consistency is the only criterion for the quality of a theory in logic-based approaches to reasoning about actions. This work goes beyond that and contributes to the metatheory of actions by investigating what other properties a good domain description should have. We state some metatheoretical postulates concerning this sore spot. When all postulates are satisfied we call the action theory modular. Besides being easier to understand and more elaboration tolerant in McCarthy’s sense, modular theories have interesting properties. We point (...)
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  • The actorʼs view of automated planning and acting: A position paper.Malik Ghallab, Dana Nau & Paolo Traverso - 2014 - Artificial Intelligence 208 (C):1-17.
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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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  • Defeasibly successful action.Richmond H. Thomason - unknown
    “Philosophy of action” is a recognized specialty in contemporary philosophy, and the literature on action is fairly extensive: see, for instance, (Care & Landesman 1968; Goldman 1970; Hornsby 1980). The relation of actions to their effects is formulated most clearly in the more specialized literature on the logic of action; see (Belnap & Perloff 1988; Chellas 1992; Czelakowski 1996; Segerberg 1982).
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