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Books on the topic 'Probabilistic logics'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Probabilistic logics and probabilistic networks. Dordrecht: Springer, 2011.

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2

Haenni, Rolf, Jan-Willem Romeijn, Gregory Wheeler, and Jon Williamson. Probabilistic Logics and Probabilistic Networks. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0008-6.

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3

Huynh, Van-Nam, Yoshiteru Nakamori, Hiroakira Ono, Jonathan Lawry, Vkladik Kreinovich, and Hung T. Nguyen, eds. Interval / Probabilistic Uncertainty and Non-Classical Logics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77664-2.

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4

Katoen, Joost-Pieter. Formal Methods for Real-Time and Probabilistic Systems: 5th International AMAST Workshop, ARTS99 Bamberg, Germany, May 2628, 1999 Proceedings. Berlin: Springer-Verlag Berlin Heidelberg, 1999.

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5

Paass, Gerhard. Probabilistic logic. Sankt Augustin: Gesellschaft fur Mathematik und Datenverarbeitung, 1987.

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6

Goertzel, Ben, Matthew Iklé, Izabela Freire Goertzel, and Ari Heljakka. Probabilistic Logic Networks. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-76872-4.

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7

De Raedt, Luc, Paolo Frasconi, Kristian Kersting, and Stephen Muggleton, eds. Probabilistic Inductive Logic Programming. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78652-8.

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8

Coletii, Giulianella, and Romano Scozzafava. Probabilistic Logic in a Coherent Setting. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0474-9.

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9

Fisseler, Jens. Learning and modeling with probabilistic conditional logic. Heidelberg: Ios Press, 2010.

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10

1964-, Raedt Luc de, ed. Probabilistic inductive logic programming: Theory and applications. Berlin: Springer, 2008.

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11

Ognjanović, Zoran, ed. Probabilistic Extensions of Various Logical Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52954-3.

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12

Solozhentsev, E. D. Risk Management Technologies: With Logic and Probabilistic Models. Dordrecht: Springer Netherlands, 2012.

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13

Probabilistic similarity networks. Cambridge, Mass: MIT Press, 1991.

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14

Solozhent︠s︡ev, E. D. Scenario logic and probabilistic management of risk in business and engineering. 2nd ed. New York: Springer, 2009.

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15

Solozhent︠s︡ev, E. D. Scenario logic and probabilistic management of risk in business and engineering. 2nd ed. New York: Springer, 2009.

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16

Solojentsev, Evgueni D. Scenario Logic and Probabilistic Management of Risk in Business and Engineering. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-77946-1.

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17

Scenario logic and probabilistic management of risk in business and engineering. 2nd ed. New York: Springer, 2009.

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18

Scenario logic and probabilistic management of risk in business and engineering. New York: Springer, 2005.

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19

Saminger-Platz, Susanne, and Radko Mesiar, eds. On Logical, Algebraic, and Probabilistic Aspects of Fuzzy Set Theory. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28808-6.

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20

Representing and reasoning with probabilistic knowledge: A logical approach to probabilities. Cambridge, Mass: MIT Press, 1990.

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21

Probabilistic logic networks: A comprehensive conceptual, mathematical and computational framework for uncertain inference. New York: Springer, 2008.

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22

Deductive, probabilistic, and inductive dependence: An axiomatic study in probability semantics. Frankfurt am Main: P. Lang, 1997.

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23

Alfaro, Luca. Process Algebra and Probabilistic Methods. Performance Modelling and Verification: Joint International Workshop, PAPM-PROBMIV 2001 Aachen, Germany, September 12-14, 2001 Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2001.

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24

Williamson, Jon, Rolf Haenni, Gregory Wheeler, and Jan-Willem Romeijn. Probabilistic Logics and Probabilistic Networks. Springer, 2013.

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25

Haenni, Rolf, Gregory Wheeler, and Jan-Willem Romeijn. Probabilistic Logics and Probabilistic Networks. Springer, 2011.

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26

Van-Nam, Huynh, ed. Interval/probabilistic uncertainty and non-classical logics. Berlin: Springer, 2008.

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27

Leitgeb, Hannes. Probability in Logic. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.35.

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This chapter is about probabilistic logics: that is, systems of logic in which logical consequence is defined in probabilistic terms. The chapter starts with some historical background, covering the longstanding association of the study of logic with probability. There follows a clarification of the terms to be used. Then these systems of probabilistic logic are classified and some key references given. Two chief classifications are presented. Then one class of probabilistic logics, those that derive from Ernest Adams’ work, is presented in more detail. In reviewing Adams’ work, six types of semantics are presented. These are examined via six case studies.

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28

Interval / Probabilistic Uncertainty and Non-classical Logics (Advances in Intelligent and Soft Computing Book 46). Springer, 2008.

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29

Bělohlávek, Radim, Joseph W. Dauben, and George J. Klir. Fuzzy Logic in the Narrow Sense. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190200015.003.0004.

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The chapter examines the various propositional and predicate many-valued logics that were studied prior to the emergence of the concept of a fuzzy set in the mid-1960s, which led to the genesis of fuzzy logic in broad and narrow senses. Early ideas regarding formal systems of fuzzy logic allowed for deduction from partially true premises to partially true consequences, as suggested first by Goguen in the 1960s and further developed by Pavelka in the 1970s, and these ideas were developed from the 1990s onward. The systematic development of fuzzy logics based on t-norms and their residua, pursued under the leadership of Hájek in the 1990s, is discussed in some detail. An overview is presented of fuzzy logics that are not truth-functional, such as probabilistic, possibilistic and modal fuzzy logic. The chapter concludes by reviewing relevant additional issues, such as issues of computational complexity for fuzzy logic or higher-order fuzzy logics.

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30

Pták, Pavel, and Sylvia Pulmannová. Orthomodular Structures as Quantum Logics: Intrinsic Properties, State Space and Probabilistic Topics (Fundamental Theories of Physics). Springer, 1991.

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31

Paolo Frasconi,Luc De Raedt,Kristian Kersting. Probabilistic Inductive Logic Programming. Springer, 2008.

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32

Frasconi, Paolo, Kristian Kersting, Luc De Raedt, and Stephen H. Muggleton. Probabilistic Inductive Logic Programming. Springer, 2008.

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33

Scozzafava, R., and Giulianella Coletti. Probabilistic Logic in a Coherent Setting. Springer, 2012.

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34

Coletti, G., and R. Scozzafava. Probabilistic Logic in a Coherent Setting (Trends in Logic). Springer, 2002.

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35

Coletti, G., and R. Scozzafava. Probabilistic Logic in a Coherent Setting (Trends in Logic). Springer, 2002.

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36

Ognjanović, Zoran. Probabilistic Extensions of Various Logical Systems. Springer International Publishing AG, 2020.

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37

Ognjanović, Zoran. Probabilistic Extensions of Various Logical Systems. Springer International Publishing AG, 2021.

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38

Risk Management Technologies With Logic And Probabilistic Models. Springer, 2012.

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39

Solozhentsev, E. D. Risk Management Technologies: With Logic and Probabilistic Models. Springer, 2012.

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40

Solozhentsev, E. D. Risk Management Technologies: With Logic and Probabilistic Models. Springer, 2014.

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41

Goertzel, Ben, Matthew Iklé, Izabela Freire Goertzel, and Ari Heljakka. Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference. Springer London, Limited, 2008.

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42

Goertzel, Ben, Matthew Iklé, Izabela Freire Goertzel, and Ari Heljakka. Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference. Springer, 2010.

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43

Galavotti, Maria Carla. The Origins of Probabilistic Epistemology. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.8.

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The notion of probability received great attention from 20th-century mathematicians and philosophers alike. This chapter focusses on a number of thinkers who not only devoted great efforts to the notion of probability and its foundations, but also developed a thoroughly probabilistic epistemological perspective. Special attention will be paid to Hans Reichenbach, Harold Jeffreys, and Bruno de Finetti. Although these authors embraced diverging interpretations of probability, namely frequentism in the case of Reichenbach, logicism in the case of Jeffreys, and subjectivism in the case of de Finetti, they shared the conviction that probability is an essential ingredient not just of science, but of human knowledge at large, and laid the foundations of a probabilistic approach to epistemology that is today mainstream.

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44

Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning. River Publishers, 2018.

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45

Riguzzi, Fabrizio. Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning. River Publishers, 2022.

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46

Riguzzi, Fabrizio. Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning. River Publishers, 2018.

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47

Riguzzi, Fabrizio. Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning. River Publishers, 2019.

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48

Riguzzi, Fabrizio. Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning. River Publishers, 2022.

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49

Riguzzi, Fabrizio. Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning. River Publishers, 2022.

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50

McIver, Annabelle, and Charles Carroll Morgan. Abstraction, Refinement and Proof for Probabilistic Systems. Springer, 2010.

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