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  1. A Computational Learning Semantics for Inductive Empirical Knowledge.Kevin T. Kelly - 2014 - In Alexandru Baltag & Sonja Smets (eds.), Johan van Benthem on Logic and Information Dynamics. Cham, Switzerland: Springer International Publishing. pp. 289-337.
    This chapter presents a new semantics for inductive empirical knowledge. The epistemic agent is represented concretely as a learner who processes new inputs through time and who forms new beliefs from those inputs by means of a concrete, computable learning program. The agent’s belief state is represented hyper-intensionally as a set of time-indexed sentences. Knowledge is interpreted as avoidance of error in the limit and as having converged to true belief from the present time onward. Familiar topics are re-examined within (...)
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  • Dynamic Consequence and Public Announcement.Andrés Cordón Franco, Hans van Ditmarsch & Angel Nepomuceno - 2013 - Review of Symbolic Logic 6 (4):659-679.
    In van Benthem (2008), van Benthem proposes a dynamic consequence relation defined as${\psi _1}, \ldots,{\psi _n}{ \models ^d}\phi \,{\rm{iff}}{ \models ^{pa}}[{\psi _1}] \ldots [{\psi _n}]\phi,$where the latter denotes consequence in public announcement logic, a dynamic epistemic logic. In this paper we investigate the structural properties of a conditional dynamic consequence relation$\models _{\rm{\Gamma }}^d$extending van Benthem’s proposal. It takes into account a set of background conditions Γ, inspired by Makinson (2003) wherein Makinson calls this reasoning ‘modulo’ a set Γ. In the (...)
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  • Formal learning theory.Oliver Schulte - 2008 - Stanford Encyclopedia of Philosophy.
    Formal learning theory is the mathematical embodiment of a normative epistemology. It deals with the question of how an agent should use observations about her environment to arrive at correct and informative conclusions. Philosophers such as Putnam, Glymour and Kelly have developed learning theory as a normative framework for scientific reasoning and inductive inference.
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  • DEL-sequents for progression.Guillaume Aucher - 2011 - Journal of Applied Non-Classical Logics 21 (3-4):289-321.
    Dynamic Epistemic Logic (DEL) deals with the representation and the study in a multi-agent setting of knowledge and belief change. It can express in a uniform way epistemic statements about: 1. what is true about an initial situation 2. what is true about an event occurring in this situation 3. what is true about the resulting situation after the event has occurred. We axiomatize within the DEL framework what we can infer about (iii) given (i) and (ii). Given three formulas (...)
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  • Planning-based knowing how: A unified approach.Yanjun Li & Yanjing Wang - 2021 - Artificial Intelligence 296 (C):103487.
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  • Epistemic planning: Perspectives on the special issue.Vaishak Belle, Thomas Bolander, Andreas Herzig & Bernhard Nebel - 2023 - Artificial Intelligence 316 (C):103842.
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  • Knowledge-based programs as succinct policies for partially observable domains.Bruno Zanuttini, Jérôme Lang, Abdallah Saffidine & François Schwarzentruber - 2020 - Artificial Intelligence 288 (C):103365.
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  • A Composable Language for Action Models.Tim French, James Hales & Edwin Tay - 2014 - In Rajeev Goré, Barteld Kooi & Agi Kurucz (eds.), Advances in Modal Logic, Volume 10: Papers From the Tenth Aiml Conference, Held in Groningen, the Netherlands, August 2014. London, England: CSLI Publications. pp. 197-216.
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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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  • Generalized DEL-sequents.Guillaume Aucher, Bastien Maubert & François Schwarzentruber - 2012 - In Luis Farinas del Cerro, Andreas Herzig & Jerome Mengin (eds.), Logics in Artificial Intelligence. Springer. pp. 54--66.
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