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  1. Roles, Rigidity and Quantification in Epistemic Logic.Wesley H. Holliday & John Perry - 2014 - In Alexandru Baltag & Sonja Smets (eds.), Johan van Benthem on Logic and Information Dynamics. Cham, Switzerland: Springer International Publishing. pp. 591-629.
    Epistemic modal predicate logic raises conceptual problems not faced in the case of alethic modal predicate logic : Frege’s “Hesperus-Phosphorus” problem—how to make sense of ascribing to agents ignorance of necessarily true identity statements—and the related “Hintikka-Kripke” problem—how to set up a logical system combining epistemic and alethic modalities, as well as others problems, such as Quine’s “Double Vision” problem and problems of self-knowledge. In this paper, we lay out a philosophical approach to epistemic predicate logic, implemented formally in Melvin (...)
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  • Hidden protocols: Modifying our expectations in an evolving world.Hans van Ditmarsch, Sujata Ghosh, Rineke Verbrugge & Yanjing Wang - 2014 - Artificial Intelligence 208 (1):18--40.
    When agents know a protocol, this leads them to have expectations about future observations. Agents can update their knowledge by matching their actual observations with the expected ones. They eliminate states where they do not match. In this paper, we study how agents perceive protocols that are not commonly known, and propose a semantics-driven logical framework to reason about knowledge in such scenarios. In particular, we introduce the notion of epistemic expectation models and a propositional dynamic logic-style epistemic logic for (...)
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  • Arbitrary arrow update logic.Hans van Ditmarsch, Wiebe van der Hoek, Barteld Kooi & Louwe B. Kuijer - 2017 - Artificial Intelligence 242 (C):80-106.
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  • 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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  • Automated temporal equilibrium analysis: Verification and synthesis of multi-player games.Julian Gutierrez, Muhammad Najib, Giuseppe Perelli & Michael Wooldridge - 2020 - Artificial Intelligence 287 (C):103353.
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