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Mu-calculus (a.k.a. μTL) is built up from modal/dynamic logic via adding the least fixpoint operator μ. This type of logic has attracted increasing attention since Kozen’s seminal work. P μTL is a succinct probabilistic extension of the standard μTL obtained by making the modal operators probabilistic. Properties of this logic, such as expressiveness and satisfiability decision, have been studied elsewhere. We consider another important problem: the axiomatization of that logic. By extending the approaches of Kozen and Walukiewicz, we present an axiom system for P μTL. In addition, we show that the axiom system is complete for aconjunctive formulas.


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An Axiom System of Probabilistic Mu-Calculus

Show Author's information Wanwei Liu( )Junnan XuDavid N. JansenAndrea TurriniLijun Zhang
College of Computer Science, National University of Defense Technology, Changsha 410073, China
State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100039, China
Institute of Intelligent Software, Guangzhou 511458, China

Abstract

Mu-calculus (a.k.a. μTL) is built up from modal/dynamic logic via adding the least fixpoint operator μ. This type of logic has attracted increasing attention since Kozen’s seminal work. P μTL is a succinct probabilistic extension of the standard μTL obtained by making the modal operators probabilistic. Properties of this logic, such as expressiveness and satisfiability decision, have been studied elsewhere. We consider another important problem: the axiomatization of that logic. By extending the approaches of Kozen and Walukiewicz, we present an axiom system for P μTL. In addition, we show that the axiom system is complete for aconjunctive formulas.

Keywords: PμTL, axiom system, aconjunctive formula, tableau approach

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Received: 24 September 2020
Revised: 15 October 2020
Accepted: 19 October 2020
Published: 29 September 2021
Issue date: April 2022

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Acknowledgements

W. W. Liu is supported by the National Science Foundation of China (No. 61872371), the Open Fund from the State Key Laboratory of High Performance Computing of China (HPCL) (No. 2020001-07), and the National Key Research and Development Program of China (No. 2018YFB0204301). L. J. Zhang is supported by the Guangdong Science and Technology (No. 2018B010107004) and the National Science Foundation of China (Nos. 61761136011 and 61836005).

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