Results for 'Probabilistic logic'

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  1.  50
    Advances in Contemporary Logic and Computer Science Proceedings of the Eleventh Brazilian Conference on Mathematical Logic, May 6-10, 1996, Salvador, Bahia, Brazil. [REVIEW]Walter A. Carnielli, Itala M. L. D'ottaviano & Brazilian Conference on Mathematical Logic - 1999
    This volume presents the proceedings from the Eleventh Brazilian Logic Conference on Mathematical Logic held by the Brazilian Logic Society (co-sponsored by the Centre for Logic, Epistemology and the History of Science, State University of Campinas, Sao Paulo) in Salvador, Bahia, Brazil. The conference and the volume are dedicated to the memory of professor Mario Tourasse Teixeira, an educator and researcher who contributed to the formation of several generations of Brazilian logicians. Contributions were made from leading (...)
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  2.  41
    Probabilistic Logic Under Coherence, Model-Theoretic Probabilistic Logic, and Default Reasoning in System P.Veronica Biazzo, Angelo Gilio, Thomas Lukasiewicz & Giuseppe Sanfilippo - 2002 - Journal of Applied Non-Classical Logics 12 (2):189-213.
    We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail, we explore how probabilistic reasoning under coherence is related to model- theoretic probabilistic reasoning and to default reasoning in System . In particular, we show that the notions of g-coherence and of g-coherent entailment can be expressed by combining notions in model-theoretic probabilistic logic with concepts from default reasoning. Moreover, we show that probabilistic reasoning under coherence is (...)
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  3.  10
    A Probabilistic Extension of Intuitionistic Logic.Z. Ognjanovic & Z. Markovic - 2003 - Mathematical Logic Quarterly 49 (4):415.
    We introduce a probabilistic extension of propositional intuitionistic logic. The logic allows making statements such as P≥sα, with the intended meaning “the probability of truthfulness of α is at least s”. We describe the corresponding class of models, which are Kripke models with a naturally arising notion of probability, and give a sound and complete infinitary axiomatic system. We prove that the logic is decidable.
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  4. 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 (...)
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  5.  68
    Probabilistic Logic Under Coherence, Conditional Interpretations, and Default Reasoning.Angelo Gilio - 2005 - Synthese 146 (1-2):139-152.
    We study a probabilistic logic based on the coherence principle of de Finetti and a related notion of generalized coherence (g-coherence). We examine probabilistic conditional knowledge bases associated with imprecise probability assessments defined on arbitrary families of conditional events. We introduce a notion of conditional interpretation defined directly in terms of precise probability assessments. We also examine a property of strong satisfiability which is related to the notion of toleration well known in default reasoning. In our framework (...)
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  6.  39
    Hybrid Probabilistic Logic Programs as Residuated Logic Programs.Carlos Viegas Damásio & Luís Moniz Pereira - 2002 - Studia Logica 72 (1):113 - 138.
    In this paper we show the embedding of Hybrid Probabilistic Logic Programs into the rather general framework of Residuated Logic Programs, where the main results of (definite) logic programming are validly extrapolated, namely the extension of the immediate consequences operator of van Emden and Kowalski. The importance of this result is that for the first time a framework encompassing several quite distinct logic programming semantics is described, namely Generalized Annotated Logic Programs, Fuzzy Logic (...)
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  7.  21
    Probabilistic Logic and Probabilistic Networks. Haenni, R., Romeijn, J.-W., Wheeler, G. & Williamson, J. - unknown
    While in principle probabilistic logics might be applied to solve a range of problems, in practice they are rarely applied at present. This is perhaps because they seem disparate, complicated, and computationally intractable. However, we shall argue in this programmatic paper that several approaches to probabilistic logic into a simple unifying framework: logically complex evidence can be used to associate probability intervals or probabilities with sentences.
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  8.  74
    Objective Bayesian Probabilistic Logic.Jon Williamson - manuscript
    This paper develops connections between objective Bayesian epistemology—which holds that the strengths of an agent’s beliefs should be representable by probabilities, should be calibrated with evidence of empirical probability, and should otherwise be equivocal—and probabilistic logic. After introducing objective Bayesian epistemology over propositional languages, the formalism is extended to handle predicate languages. A rather general probabilistic logic is formulated and then given a natural semantics in terms of objective Bayesian epistemology. The machinery of objective Bayesian nets (...)
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  9.  7
    The Probabilistic Logic of Eusebius Amort.Miroslav Hanke* - 2019 - Early Science and Medicine 24 (2):186-211.
    While classical sources including Aristotle, Cicero and Boëthius addressed different notions of probability, medieval contributions to probability seem rather scarce. The situation changes during the Second Scholasticism with the post-Tridentine debates on “probable opinion” in moral theology and the introduction of “moral necessity” and “moral implication” in the debates on compatibilism and theological optimism. The eighteenth-century transformation of scholastic philosophy was marked, among other characteristics, by a gravitation towards the early modern scientific revolution. In his Philosophia Pollingana ad normam Burgundicae, (...)
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  10. Probabilistic Dynamic Epistemic Logic.Barteld P. Kooi - 2003 - Journal of Logic, Language and Information 12 (4):381-408.
    In this paper I combine the dynamic epistemic logic ofGerbrandy (1999) with the probabilistic logic of Fagin and Halpern (1994). The resultis a new probabilistic dynamic epistemic logic, a logic for reasoning aboutprobability, information, and information change that takes higher orderinformation into account. Probabilistic epistemic models are defined, and away to build them for applications is given. Semantics and a proof systemis presented and a number of examples are discussed, including the MontyHall Dilemma.
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  11.  17
    Jesuit Probabilistic Logic Between Scholastic and Academic Philosophy.Miroslav Hanke - 2019 - History and Philosophy of Logic 40 (4):355-373.
    There is a well-documented paradigm-shift in eighteenth century Jesuit philosophy and science, at the very least in Central Europe: traditional scholastic version of Aristotelianism were replaced by early modern rationalism and early modern science and mathematics. In the field of probability, this meant that the traditional Jesuit engagement with probability, uncertainty, and truthlikeness could translate into mathematical language, and can be analysed against the background of the accounts of probability, pre-mathematical Jesuit logic, Wolff's conceptual analysis, and Bernoullian mathematisation. The (...)
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  12.  59
    From Worlds to Probabilities: A Probabilistic Semantics for Modal Logic.Charles B. Cross - 1993 - Journal of Philosophical Logic 22 (2):169 - 192.
    I give a probabilistic semantics for modal logic in which modal operators function as quantifiers over Popper functions in probabilistic model sets, thereby generalizing Kripke's semantics for modal logic.
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  13.  3
    Probabilistic Logic.Armin Schulz - 2010 - In Jon Williamson & Federica Russo (eds.), Key Terms in Logic. The key terms in philosophy. London: Continuum. pp. 57.
    Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students (...)
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  14.  4
    Probabilistic Logic.Armin Schulz - 2010 - In Jon Williamson & Federica Russo (eds.), Key Terms in Logic. The key terms in philosophy. London, U.K.: Continuum. pp. 57.
    Key Terms in Logic offers the ideal introduction to this core area in the study of philosophy, providing detailed summaries of the important concepts in the study of logic and the application of logic to the rest of philosophy. A brief introduction provides context and background, while the following chapters offer detailed definitions of key terms and concepts, introductions to the work of key thinkers and lists of key texts. Designed specifically to meet the needs of students (...)
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  15.  6
    Probabilistic Entailment and a Non-Probabilistic Logic.Kevin Knight - 2003 - Logic Journal of the IGPL 11 (3):353-365.
    In this paper we present a probabilistic notion of entailment for finite sets of premises, which has classical entailment as a special case, and show that it is well defined; i.e., that the problem of whether a sentence is entailed by a set of premises is computable. Further we present a natural deductive system and prove that it is the strongest deductive system possible without referring to probabilities.
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  16.  56
    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 (...)
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  17.  98
    A Logic For Inductive Probabilistic Reasoning.Manfred Jaeger - 2005 - Synthese 144 (2):181-248.
    Inductive probabilistic reasoning is understood as the application of inference patterns that use statistical background information to assign (subjective) probabilities to single events. The simplest such inference pattern is direct inference: from “70% of As are Bs” and “a is an A” infer that a is a B with probability 0.7. Direct inference is generalized by Jeffrey’s rule and the principle of cross-entropy minimization. To adequately formalize inductive probabilistic reasoning is an interesting topic for artificial intelligence, as an (...)
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  18. Voting in Search of the Public Good: The Probabilistic Logic of Majority Judgments.James Hawthorne - manuscript
    I argue for an epistemic conception of voting, a conception on which the purpose of the ballot is at least in some cases to identify which of several policy proposals will best promote the public good. To support this view I first briefly investigate several notions of the kind of public good that public policy should promote. Then I examine the probability logic of voting as embodied in two very robust versions of the Condorcet Jury Theorem and some related (...)
     
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  19.  9
    Probabilistic Logic of Quantum Observations.A. Sernadas, J. Rasga, C. Sernadas, L. Alcácer & A. B. Henriques - 2019 - Logic Journal of the IGPL 27 (3):328-370.
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  20.  11
    A First-Order Probabilistic Logic with Approximate Conditional Probabilities.N. Ikodinovi, M. Ra Kovi, Z. Markovi & Z. Ognjanovi - 2014 - Logic Journal of the IGPL 22 (4):539-564.
  21.  6
    Assembling a Consistent Set of Sentences in Relational Probabilistic Logic with Stochastic Independence.Cassio Polpo de Campos, Fabio Gagliardi Cozman & José Eduardo Ochoa Luna - 2009 - Journal of Applied Logic 7 (2):137-154.
  22.  26
    How to Exploit Parametric Uniformity for Maximum Entropy Reasoning in a Relational Probabilistic Logic.Marc Finthammer & Christoph Beierle - 2012 - In Luis Farinas del Cerro, Andreas Herzig & Jerome Mengin (eds.), Logics in Artificial Intelligence. Springer. pp. 189--201.
  23.  10
    A New Normative Theory of Probabilistic Logic.Romas Aleliunas - 1990 - In Kyburg Henry E., Loui Ronald P. & Carlson Greg N. (eds.), Knowledge Representation and Defeasible Reasoning. Kluwer Academic Publishers. pp. 387--403.
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  24.  6
    An Expectation-Transformer Model for Probabilistic Temporal Logic.C. Morgan & A. Mciver - 1999 - Logic Journal of the IGPL 7 (6):779-804.
    We interpret the modal µ-calculus over a new model [10], to give a temporal logic suitable for systems exhibiting both probabilistic and demonic nondeterminism. The logical formulae are real-valued, and the statements are not limited to properties that hold with probability 1. In achieving that conceptual step, our technical contribution is to determine the correct quantitative generalisation of the Boolean operators: one that allows many of the standard Boolean-based temporal laws to carry over the reals with little or (...)
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  25. A Many-Valued Probabilistic Logic.F. Lepage - 2000 - Poznan Studies in the Philosophy of the Sciences and the Humanities 71:36-48.
  26. A Many-Valued Probabilistic Logic: Commentary.C. Morgan - 2000 - Poznan Studies in the Philosophy of the Sciences and the Humanities 71:36-48.
  27.  9
    Probabilistic Semantics for a Discussive Temporal Logic.Carlo Proietti & Roberto Ciuni - forthcoming - The Logica Yearbook.
    The paper introduces a probabilistic semantics for the paraconsistent temporal logic Ab presented by the authors in a previous work on future contingents. Probabilistic concepts help framing two possible interpretations of the logic in question - a `subjective' and an `objective' one - and explaining the rationale behind both of them. We also sketch a proof-method for Ab and address some considerations regarding the conceptual appeal of our proposal and its possible future developments.
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  28. A Quantitative Doxastic Logic for Probabilistic Processes and Applications to Information-Hiding.Simon Kramer, Catuscia Palamidessi, Roberto Segala, Andrea Turrini & Christelle Braun - 2009 - Journal of Applied Non-Classical Logics 19 (4):489-516.
    We introduce a novel modal logic, namely the doxastic μ-calculus with error control, and propose a formalization of probabilistic anonymity and oblivious transfer in the logic, and the validation of these formalizations on implementations formalized in probabilistic CCS. The distinguishing feature of our logic is to provide a combination of dynamic operators for belief with a control on the possible error of apprehension of the perceived reality, and for internalized probability. Both operators are dynamic thanks (...)
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  29.  37
    Inductive Logic with Causal Modalities: A Probabilistic Approach.Soshichi Uchii - 1972 - Philosophy of Science 39 (2):162-178.
    This paper tries to extend Hintikka's inductive logic so that we can confirm a causally necessary statement. For this purpose, a joint system of inductive logic and logic of causal modalities is constructed. This system can offer a plausible explication of the distinction between nomic and accidental universality, as well as a good formulation of a causal law. And the transition from actuality to causal necessity is construed, in this system, as essentially probabilistic; i.e. no statements (...)
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  30.  13
    The Fuzzy Logic of Chaos and Probabilistic Inference.I. Antoniou & Z. Suchanecki - 1997 - Foundations of Physics 27 (3):333-362.
    The logic of a physical system consists of the elementary observables of the system. We show that for chaotic systems the logic is not any more the classical Boolean lattice but a kind of fuzzy logic which we characterize for a class of chaotic maps. Among other interesting properties the fuzzy logic of chaos does not allow for infinite combinations of propositions. This fact reflects the instability of dynamics and it is shared also by quantum systems (...)
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  31.  49
    Lottery Semantics: A Compositional Semantics for Probabilistic First-Order Logic with Imperfect Information.Pietro Galliani & Allen L. Mann - 2013 - Studia Logica 101 (2):293-322.
    We present a compositional semantics for first-order logic with imperfect information that is equivalent to Sevenster and Sandu’s equilibrium semantics (under which the truth value of a sentence in a finite model is equal to the minimax value of its semantic game). Our semantics is a generalization of an earlier semantics developed by the first author that was based on behavioral strategies, rather than mixed strategies.
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  32.  12
    Logic and Probabilistic Systems.Franco Montagna, Giulia Simi & Andrea Sorbi - 1996 - Archive for Mathematical Logic 35 (4):225-261.
    Following some ideas of Roberto Magari, we propose trial and error probabilistic functions, i.e. probability measures on the sentences of arithmetic that evolve in time by trial and error. The set ℐ of the sentences that get limit probability 1 is a Π3—theory, in fact ℐ can be a Π3—complete set. We prove incompleteness results for this setting, by showing for instance that for every k > 0 there are true Π3—sentences that get limit probability less than 1/2k. No (...)
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  33.  15
    First Steps Towards Probabilistic Justification Logic.Ioannis Kokkinis, Petar Maksimović, Zoran Ognjanović & Thomas Studer - 2015 - Logic Journal of the IGPL 23 (4):662-687.
  34.  21
    The Logic of Probabilistic Knowledge.Patricia Rich - forthcoming - Philosophical Studies:1-23.
    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 (...)
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  35.  24
    Franco Montagna, Giulia Simi, and Andrea Sorbi. Logic and Probabilistic Systems. Archive for Mathematical Logic, Vol. 35 , Pp. 225–261. [REVIEW]J. B. Paris - 2000 - Bulletin of Symbolic Logic 6 (2):223-225.
  36.  64
    The Probabilistic Argument for a Non-Classical Logic of Quantum Mechanics.Patrick Suppes - 1966 - Philosophy of Science 33 (1/2):14-21.
    The aim of this paper is to state the single most powerful argument for use of a non-classical logic in quantum mechanics. In outline the argument is the following. The working logic of a science is the logic of the events and propositions to which probabilities are assigned. A probability should be assigned to every element of the algebra of events. In the case of quantum mechanics probabilities may be assigned to events but not, without restriction, to (...)
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  37.  19
    Probabilistic Semantics for First‐Order Logic.Hugues Leblanc - 1979 - Mathematical Logic Quarterly 25 (32):497-509.
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  38. Probabilistic Semantics for Formal Logic.Charles Morgan & Hugues Leblanc - 1983 - Notre Dame Journal of Formal Logic 24:161-180.
  39.  15
    Probabilistic Semantics for Intuitionistic Logic.C. G. Morgan & H. Leblanc - 1983 - Notre Dame Journal of Formal Logic 24 (2):161-180.
  40.  32
    There is a Probabilistic Semantics for Every Extension of Classical Sentence Logic.Charles G. Morgan - 1982 - Journal of Philosophical Logic 11 (4):431 - 442.
  41.  14
    Minimal Doxastic Logic: Probabilistic and Other Completeness Theorems.Peter Milne - 1993 - Notre Dame Journal of Formal Logic 34 (4):499-526.
  42.  5
    Probabilistic Reasoning in a Classical Logic.K. S. Ng & J. W. Lloyd - 2009 - Journal of Applied Logic 7 (2):218-238.
  43.  32
    A Many-Valued Probabilistic Conditional Logic.François Lepage - 2000 - In N. Shanks & R. Gardner (eds.), Logic, Probability and Science. Atlanta: Rodopi. pp. 36.
  44. Random Predicate Logic I: A Probabilistic Approach to Vagueness.William A. Dembski - unknown
    Predicates are supposed to slice reality neatly in two halves, one for which the predicate holds, the other for which it fails. Yet far from being razors, predicates tend to be dull knives that mangle reality. If reality is a tomato and predicates are knives, then when these knives divide the tomato, plenty of mush remains unaccounted for. Of course some knives are sharper than others, just as some predicates are less vague than others. “x is water” is certainly sharper (...)
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  45.  6
    Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples.Henri Prade, Markus Knauff, Igor Douven & Gabriele Kern-Isberner - 2017 - Minds and Machines 27 (1):37-77.
    This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with (...)
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  46.  19
    First-Order Probabilistic Conditional Logic and Maximum Entropy.J. Fisseler - 2012 - Logic Journal of the IGPL 20 (5):796-830.
  47.  28
    A Propositional Dynamic Logic with Qualitative Probabilities.Dimitar P. Guelev - 1999 - Journal of Philosophical Logic 28 (6):575-604.
    This paper presents an w-completeness theorem for a new propositional probabilistic logic, namely, the dynamic propositional logic of qualitative probabilities (DQP), which has been introduced by the author as a dynamic extension of the logic of qualitative probabilities (Q P) introduced by Segerberg.
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  48.  28
    Review: Franco Montagna, Giulia Simi, Andrea Sorbi, Logic and Probabilistic Systems. [REVIEW]J. B. Paris - 2000 - Bulletin of Symbolic Logic 6 (2):223-225.
  49.  14
    Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples.Tarek R. Besold, Artur D’Avila Garcez, Keith Stenning, Leendert van der Torre & Michiel van Lambalgen - 2017 - Minds and Machines 27 (1):37-77.
    This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty ; and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with (...)
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  50.  25
    A “Definitive” Probabilistic Semantics for First-Order Logic.Kent Bendall - 1982 - Journal of Philosophical Logic 11 (3):255 - 278.
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