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  1. Dynamic Update with Probabilities.Johan van Benthem, Jelle Gerbrandy & Barteld Kooi - 2009 - Studia Logica 93 (1):67 - 96.
    Current dynamic-epistemic logics model different types of information change in multi-agent scenarios. We generalize these logics to a probabilistic setting, obtaining a calculus for multi-agent update with three natural slots: prior probability on states, occurrence probabilities in the relevant process taking place, and observation probabilities of events. To match this update mechanism, we present a complete dynamic logic of information change with a probabilistic character. The completeness proof follows a compositional methodology that applies to a much larger class of dynamic-probabilistic (...)
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  • Contemporary Epistemic Logic and the Lockean Thesis.Lorenz Demey - 2013 - Foundations of Science 18 (4):599-610.
    This paper studies the Lockean thesis from the perspective of contemporary epistemic logic. The Lockean thesis states that belief can be defined as ‘sufficiently high degree of belief’. Its main problem is that it gives rise to a notion of belief which is not closed under conjunction. This problem is typical for classical epistemic logic: it is single-agent and static. I argue that from the perspective of contemporary epistemic logic, the Lockean thesis fares much better. I briefly mention that it (...)
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  • Extending probabilistic dynamic epistemic logic.Joshua Sack - 2009 - Synthese 169 (2):241 - 257.
    This paper aims to extend in two directions the probabilistic dynamic epistemic logic provided in Kooi’s paper (J Logic Lang Inform 12(4):381–408, 2003) and to relate these extensions to ones made in van Benthem et al. (Proceedings of LOFT’06. Liverpool, 2006). Kooi’s probabilistic dynamic epistemic logic adds to probabilistic epistemic logic sentences that express consequences of public announcements. The paper (van Benthem et al., Proceedings of LOFT’06. Liverpool, 2006) extends (Kooi, J Logic Lang Inform 12(4):381–408, 2003) to using action models, (...)
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  • Dynamic Formal Epistemology.Patrick Girard, Olivier Roy & Mathieu Marion (eds.) - 2010 - Berlin, Germany: Springer.
    This volume is a collation of original contributions from the key actors of a new trend in the contemporary theory of knowledge and belief, that we call “dynamic epistemology”. It brings the works of these researchers under a single umbrella by highlighting the coherence of their current themes, and by establishing connections between topics that, up until now, have been investigated independently. It also illustrates how the new analytical toolbox unveils questions about the theory of knowledge, belief, preference, action, and (...)
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  • The Logic of Fast and Slow Thinking.Anthia Solaki, Francesco Berto & Sonja Smets - 2019 - Erkenntnis 86 (3):733-762.
    We present a framework for epistemic logic, modeling the logical aspects of System 1 and System 2 cognitive processes, as per dual process theories of reasoning. The framework combines non-normal worlds semantics with the techniques of Dynamic Epistemic Logic. It models non-logically-omniscient, but moderately rational agents: their System 1 makes fast sense of incoming information by integrating it on the basis of their background knowledge and beliefs. Their System 2 allows them to slowly, step-wise unpack some of the logical consequences (...)
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  • Epistemic Probability Logic Simplified.Jan van Eijck & François Schwarzentruber - 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. 158-177.
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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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  • The cognitive structure of surprise: Looking for basic principles.Emiliano Lorini & Cristiano Castelfranchi - 2007 - Topoi 26 (1):133-149.
    We develop a conceptual and formal clarification of notion of surprise as a belief-based phenomenon by exploring a rich typology. Each kind of surprise is associated with a particular phase of cognitive processing and involves particular kinds of epistemic representations (representations and expectations under scrutiny, implicit beliefs, presuppositions). We define two main kinds of surprise: mismatch-based surprise and astonishment. In the central part of the paper we suggest how a formal model of surprise can be integrated with a formal model (...)
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  • Updating on Biased Probabilistic Testimony.Leander Vignero - 2024 - Erkenntnis 89 (2):567-590.
    In this paper, I use a framework from computational linguistics, the Rational Speech Act framework, to model deceptive probabilistic communication. This account allows agents to discount for the biases they perceive their interlocutors to have. This way, agents can update their credences with the perceived interests of others in mind.
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  • Non-Classical Probabilities for Decision Making in Situations of Uncertainty.Dominik Klein, Ondrej Majer & Soroush Rafiee Rad - 2020 - Roczniki Filozoficzne 68 (4):315-343.
    Analyzing situations where information is partial, incomplete or contradictory has created a demand for quantitative belief measures that are weaker than classic probability theory. In this paper, we compare two frameworks that have been proposed for this task, Dempster-Shafer theory and non-standard probability theory based on Belnap-Dunn logic. We show the two frameworks to assume orthogonal perspectives on informational shortcomings, but also provide a partial correspondence result. Lastly, we also compare various dynamical rules of the two frameworks, all seen as (...)
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  • Evidentiality, modality and probability.Eric McCready & Norry Ogata - 2007 - Linguistics and Philosophy 30 (2):147 - 206.
    We show in this paper that some expressions indicating source of evidence are part of propositional content and are best analyzed as special kind of epistemic modal. Our evidence comes from the Japanese evidential system. We consider six evidentials in Japanese, showing that they can be embedded in conditionals and under modals and that their properties with respect to modal subordination are similar to those of ordinary modals. We show that these facts are difficult for existing theories of evidentials, which (...)
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  • Johan van Benthem on Logic and Information Dynamics.Alexandru Baltag & Sonja Smets (eds.) - 2014 - Cham, Switzerland: Springer International Publishing.
    This book illustrates the program of Logical-Informational Dynamics. Rational agents exploit the information available in the world in delicate ways, adopt a wide range of epistemic attitudes, and in that process, constantly change the world itself. Logical-Informational Dynamics is about logical systems putting such activities at center stage, focusing on the events by which we acquire information and change attitudes. Its contributions show many current logics of information and change at work, often in multi-agent settings where social behavior is essential, (...)
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  • Quality and quantity of information exchange.Robert van Rooy - 2003 - Journal of Logic, Language and Information 12 (4):423-451.
    The paper deals with credible and relevantinformation flow in dialogs: How useful is it for areceiver to get some information, how useful is it fora sender to give this information, and how much credibleinformation can we expect to flow between sender andreceiver? What is the relation between semantics andpragmatics? These Gricean questions will be addressedfrom a decision and game-theoretical point of view.
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  • Evidentiality, modality and probability.Norry Ogata & Elin McCready - 2007 - Linguistics and Philosophy 30 (2):147-206.
    We show in this paper that some expressions indicating source of evidence are part of propositional content and are best analyzed as special kind of epistemic modal. Our evidence comes from the Japanese evidential system. We consider six evidentials in Japanese, showing that they can be embedded in conditionals and under modals and that their properties with respect to modal subordination are similar to those of ordinary modals. We show that these facts are difficult for existing theories of evidentials, which (...)
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  • The logic of probabilistic knowledge.Patricia Rich - 2020 - Philosophical Studies 177 (6):1703-1725.
    Sarah Moss’ thesis that we have probabilistic knowledge is from some perspectives unsurprising and from other perspectives hard to make sense of. The thesis is potentially transformative, but not yet elaborated in sufficient detail for epistemologists. This paper interprets Mossean probabilistic knowledge in a suitably-modified Kripke framework, thus filling in key details. It argues that probabilistic knowledge looks natural and plausible when so interpreted, and shows how the most pressing challenges to the thesis can be overcome. Most importantly, probabilistic knowledge (...)
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  • Dynamic Update with Probabilities.Johan Benthem, Jelle Gerbrandy & Barteld Kooi - 2009 - Studia Logica 93 (1):67-96.
    Current dynamic-epistemic logics model different types of information change in multi-agent scenarios. We generalize these logics to a probabilistic setting, obtaining a calculus for multi-agent update with three natural slots: prior probability on states, occurrence probabilities in the relevant process taking place, and observation probabilities of events. To match this update mechanism, we present a complete dynamic logic of information change with a probabilistic character. The completeness proof follows a compositional methodology that applies to a much larger class of dynamic-probabilistic (...)
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  • Dynamic doxastic logic: why, how, and where to?Hannes Leitgeb & Krister Segerberg - 2007 - Synthese 155 (2):167-190.
    We investigate the research programme of dynamic doxastic logic (DDL) and analyze its underlying methodology. The Ramsey test for conditionals is used to characterize the logical and philosophical differences between two paradigmatic systems, AGM and KGM, which we develop and compare axiomatically and semantically. The importance of Gärdenfors’s impossibility result on the Ramsey test is highlighted by a comparison with Arrow’s impossibility result on social choice. We end with an outlook on the prospects and the future of DDL.
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  • Probabilistic dynamic belief revision.Alexandru Baltag & Sonja Smets - 2008 - Synthese 165 (2):179 - 202.
    We investigate the discrete (finite) case of the Popper–Renyi theory of conditional probability, introducing discrete conditional probabilistic models for knowledge and conditional belief, and comparing them with the more standard plausibility models. We also consider a related notion, that of safe belief, which is a weak (non-negatively introspective) type of “knowledge”. We develop a probabilistic version of this concept (“degree of safety”) and we analyze its role in games. We completely axiomatize the logic of conditional belief, knowledge and safe belief (...)
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  • Conditional probability meets update logic.Johan van Benthem - 2003 - Journal of Logic, Language and Information 12 (4):409-421.
    Dynamic update of information states is a new paradigm in logicalsemantics. But such updates are also a traditional hallmark ofprobabilistic reasoning. This note brings the two perspectives togetherin an update mechanism for probabilities which modifies state spaces.
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  • Agreeing to disagree in probabilistic dynamic epistemic logic.Lorenz Demey - 2014 - Synthese 191 (3):409-438.
    This paper studies Aumann’s agreeing to disagree theorem from the perspective of dynamic epistemic logic. This was first done by Dégremont and Roy (J Phil Log 41:735–764, 2012) in the qualitative framework of plausibility models. The current paper uses a probabilistic framework, and thus stays closer to Aumann’s original formulation. The paper first introduces enriched probabilistic Kripke frames and models, and various ways of updating them. This framework is then used to prove several agreement theorems, which are natural formalizations of (...)
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  • Conditioning and Interpretation Shifts.Jan-Willem Romeijn - 2012 - Studia Logica 100 (3):583-606.
    This paper develops a probabilistic model of belief change under interpretation shifts, in the context of a problem case from dynamic epistemic logic. Van Benthem [4] has shown that a particular kind of belief change, typical for dynamic epistemic logic, cannot be modelled by standard Bayesian conditioning. I argue that the problems described by van Benthem come about because the belief change alters the semantics in which the change is supposed to be modelled: the new information induces a shift in (...)
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  • Interpreting an action from what we perceive and what we expect.Guillaume Aucher - 2007 - Journal of Applied Non-Classical Logics 17 (1):9-38.
    In update logic as studied by Baltag, Moss, Solecki and van Benthem, little attention is paid to the interpretation of an action by an agent, which is just assumed to depend on the situation. This is actually a complex issue that nevertheless complies to some logical dynamics. In this paper, we tackle this topic. We also deal with actions that change propositional facts of the situation. In parallel, we propose a formalism to accurately represent an agent's epistemic state based on (...)
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  • The Logic of Observation and Belief Revision in Scientific Communities.Hanna Sofie van Lee & Sonja Smets - 2020 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 51 (2):243-266.
    Scientists collect evidence in order to confirm or falsify scientific theories. Unfortunately, scientific evidence may sometimes be false or deceiving and as a consequence lead individuals to believe in a false theory. By interaction between scientists, such false beliefs may spread through the entire community. There is currently a debate about the effect of various network configurations on the epistemic reliability of scientific communities. To contribute to this debate from a logical perspective, this paper introduces an epistemic logical framework of (...)
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  • Communication strategies in games.Jelle Gerbrandy - 2007 - Journal of Applied Non-Classical Logics 17 (2):197-211.
    We formulate a formal framework in which we combine the theory of dynamic epistemic logic and the theory of games. In particular, we show how we can use tools of dynamic epistemic logic to reason about information change ? and in particular, the effect of communication acts ? in such a game of imperfect information. We show how this framework allows for the formulation of specific assumptions in pragmatics of communication, as well as the formulation of general results about the (...)
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  • Regression and progression in stochastic domains.Vaishak Belle & Hector J. Levesque - 2020 - Artificial Intelligence 281 (C):103247.
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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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  • Modal semantics for reasoning with probability and uncertainty.Nino Guallart - forthcoming - Logic Journal of the IGPL.
    This paper belongs to the field of probabilistic modal logic, focusing on a comparative analysis of two distinct semantics: one rooted in Kripke semantics and the other in neighbourhood semantics. The primary distinction lies in the following: The latter allows us to adequately express belief functions (lower probabilities) over propositions, whereas the former does not. Thus, neighbourhood semantics is more expressive. The main part of the work is a section in which we study the modal equivalence between probabilistic Kripke models (...)
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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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