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A General Framework for Probabilistic Characterizing Formulae [chapter]

Joshua Sack, Lijun Zhang
2012 Lecture Notes in Computer Science  
Recently, a general framework on characteristic formulae was proposed by Aceto et al.  ...  Relation to Related Work: Our theory builds on a recent paper by Aceto et al. [1] , where a general framework is introduced for constructing non-probabilistic characteristic formulae over transition systems  ...  Lijun Zhang is partly supported by MT-LAB, a VKR Centre of Excellence.  ... 
doi:10.1007/978-3-642-27940-9_26 fatcat:dfdvw77ecfewvlhuzdqedboplq

PROBABILISTIC ROLE MODELS AND THE GUARDED FRAGMENT

MANFRED JAEGER
2006 International Journal of Uncertainty Fuzziness and Knowledge-Based Systems  
We propose a uniform semantic framework for interpreting probabilistic concept subsumption and probabilistic role quantification through statistical sampling distributions.  ...  This general semantic principle serves as the foundation for the development of a probabilistic version of the guarded fragment of first-order logic.  ...  The resulting probabilistic guarded fragment provides a general framework in which many probabilistic description logics can be embedded.  ... 
doi:10.1142/s0218488506003820 fatcat:ynzfmvfzgjg3fmjcnol6ucl2pa

Probabilistic Disjunctive Logic Programming [article]

Liem Ngo
2013 arXiv   pre-print
In this paper we propose a framework for combining Disjunctive Logic Programming and Poole's Probabilistic Horn Abduction. We use the concept of hypothesis to specify the probability structure.  ...  We define the concepts of explanation and partial explanation of a formula, and use them to determine the default probability distribution(s) induced by a program.  ...  Peter Haddawy, for his various help in the presentation of the paper and in other things . The comments of the anonymous reviewers are appreciated.  ... 
arXiv:1302.3592v1 fatcat:fcac7fb45fhwtomcrq5jxkksau

Non-monotonic Negation in Hybrid Probabilistic Logic Programs

Emad Saad, Enrico Pontelli
2006 International Symposium on Artificial Intelligence and Mathematics  
Furthermore, it makes it clearer how to characterize non-monotonic negation in probabilistic logic programming frameworks for commonsense reasoning.  ...  In [23] , a new Hybrid Probabilistic Logic Programs framework has been proposed, and a new semantics has been developed to enable encoding and reasoning about real-world applications.  ...  We developed a semantical characterization of the extended framework, which relies on a probabilistic generalization of the well-founded semantics and stable model semantics, originally developed for normal  ... 
dblp:conf/isaim/SaadP06 fatcat:a6js7mw6rnhc7f53vlddtn2d4u

Page 5876 of Mathematical Reviews Vol. , Issue 98I [page]

1998 Mathematical Reviews  
Summary: “This paper describes a general framework for mod- eling fairness for communicating processes, based on the notion of fair traces.  ...  In this paper we present a basis for carrying over such inferences to a probabilistic setting.  ... 

The Ackermann Award 2013

Anuj Dawar, Thomas A. Henzinger, Damian Niwiński, Marc Herbstritt
2013 Annual Conference for Computer Science Logic  
Besides providing financial support for the Ackermann Award, the Kurt Gödel Society has also committed to inviting the recipient of the Award for a special lecture to be given to the Society in Vienna.  ...  The 2013 Ackermann Award was open to PhD dissertations in topics specified by the CSL and LICS conferences, which were formally accepted as theses for the award of a PhD degree at a university or equivalent  ...  The members of the jury were: Thierry Coquand (Chalmers University of Gothenburg), Anuj Dawar (University of Cambridge), the president of EACSL, Thomas A.  ... 
doi:10.4230/lipics.csl.2013.1 dblp:conf/csl/DawarHN13 fatcat:7srrdd44gve7jhsrx5dzg5jcne

On A Theory of Probabilistic Deductive Databases [article]

Laks V. S. Lakshmanan, Fereidoon Sadri
2003 arXiv   pre-print
We develop a framework for probabilistic deductive databases by associating confidence levels with the facts and rules of a classical deductive database.  ...  We propose a framework for modeling uncertainty where both belief and doubt can be given independent, first-class status.  ...  Acknowledgments The authors would like to thank the anonymous referees for their careful reading and their comments, many of which have resulted in significant improvements to the paper.  ... 
arXiv:cs/0312043v1 fatcat:hhcmneo5abhjjgkly7bxhbmg24

Analysing Biochemical Oscillation through Probabilistic Model Checking

Paolo Ballarini, Radu Mardare, Ivan Mura
2009 Electronical Notes in Theoretical Computer Science  
Relaying on temporal logic formalisms we investigate the possibility for a query language for stating biological properties of a system's model.  ...  Then we develop a number of (probabilistic) queries that we verify against a Markov chain model of the oscillator, through the probabilistic model checler PRISM.  ...  Hence in the discrete-state modelling framework non-probabilistic model checking (i.e. standard temporal logic) has to be used to perform qualitative analysis, whereas probabilistic/stochastic is needed  ... 
doi:10.1016/j.entcs.2009.02.002 fatcat:xvikdggc35etvclo4jld4t3nnu

Probabilisitc Logic Programming under Maximum Entropy [chapter]

Thomas Lukasiewicz, Gabriele Kern-Isberner
1999 Lecture Notes in Computer Science  
We then present an efcient linear programming characterization for the problem of deciding whether a probabilistic logic program is satis able.  ...  Finally, and as a central contribution of this paper, we introduce an e cient technique for approximative probabilistic logic programming under maximum entropy.  ...  Paris and Vencovsk a 20] investigate the foundations of consistent probabilistic inference and set up postulates that characterize ME-inference uniquely within that framework.  ... 
doi:10.1007/3-540-48747-6_26 fatcat:755nawrsqjhmzcy2wp4fwocssy

STATISTICAL RELATIONAL LEARNING: A STATE-OF-THE-ART REVIEW

Muhamet Kastrati, Marenglen Biba
2019 Journal of Engineering Technology and Applied Sciences  
It starts by giving a general overview of conventional graphical models, first-order logic and inductive logic programming approaches as needed for background.  ...  Therefore, the goal of this paper is to provide a state-of-the-art review of SRL models, in the same time to contribute to research community with a cohesive overview of state-of-the-art results for a  ...  MLNs represent a probabilistic extension of FOL with attaching weights to each formula (or clause) and are suitable to deal with complex data which are characterized with uncertainty.  ... 
doi:10.30931/jetas.594586 fatcat:qoei3pteibd6la4oqin6rvrxqi

Page 6562 of Mathematical Reviews Vol. , Issue 2000i [page]

2000 Mathematical Reviews  
We show that any context-free process can be characterized by an FLC formula up For the web version of Mathematical Reviews, see http: //www.ams.org/mathscinet  ...  In this paper a quantitative generalization of Boolean operators is suggested in order to give a temporal logic suitable for systems exhibiting probabilistic and demonic nondeterminism.  ... 

Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge [article]

Thomas Lukasiewicz
2013 arXiv   pre-print
More generally, we even show that all systems of inference rules for taxonomic and probabilistic knowledge-bases over conjunctive events are globally incomplete.  ...  We present locally complete inference rules for probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events.  ...  For a probabilistic formula (H/G)[u1, u2] and a set of tax onomic and probabilistic formulas KB, let u denote the set of all real numbers u E (0, 1] for which there exists a model Pr of KB with Pr F  ... 
arXiv:1301.7397v1 fatcat:ebghhza3uffh5md64upngc35by

Local probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events

Thomas Lukasiewicz
1999 International Journal of Approximate Reasoning  
More generally, we even show that all systems of inference rules for probabilistic deduction in taxonomic and probabilistic knowledge-bases over conjunctive events that have a limited number of probabilistic  ...  Furthermore, we show that the presented local approach is not more ecient than the linear programming approach for that framework.  ...  [15] for an example of globally complete local probabilistic deduction in a very restricted framework).  ... 
doi:10.1016/s0888-613x(99)00006-7 fatcat:f7yblvz6o5bbxpwizexlkqqpzm

A Logical Characterization of Differential Privacy via Behavioral Metrics [chapter]

Valentina Castiglioni, Konstantinos Chatzikokolakis, Catuscia Palamidessi
2018 Lecture Notes in Computer Science  
In this paper, we exploit a modeling of this framework via labeled Markov Chains (LMCs) to provide a logical characterization of differential privacy: we consider a probabilistic variant of the Hennessy-Milner  ...  Then, we focus on bisimulation semantics on nondeterministic probabilistic processes and we provide a logical characterization of generalized bisimulation metrics, namely those defined via the generalized  ...  We provide a logical characterization of the generalized bisimulation metrics from [12] , using the syntactic distance over formulae in the probabilistic extension L of HML [14] , and the notion of mimicking  ... 
doi:10.1007/978-3-030-02146-7_4 fatcat:o2mvc6qaqrat7bvanqdtrjm74q

Probabilistic verification and approximation

Richard Lassaigne, Sylvain Peyronnet
2008 Annals of Pure and Applied Logic  
We prove that, in general, there is no polynomial time randomized approximation scheme with relative error for probabilistic verification.  ...  We show how some simple randomized approximation algorithms can improve the efficiency of verification of such probabilistic specifications and can be implemented in a probabilistic model checker.  ...  Acknowledgements We would like to thank Thomas Hérault for his important participation to the development of APMC, and an anonymous referee for his pertinent remarks and suggestions.  ... 
doi:10.1016/j.apal.2007.11.006 fatcat:kjhhgvh6prglxehlpepy42afju
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