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2 edition of Implementation of symbolic model checking for probabilistic systems found in the catalog.

Implementation of symbolic model checking for probabilistic systems

David Anthony Parker

Implementation of symbolic model checking for probabilistic systems

by David Anthony Parker

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Published by University of Birmingham in Birmingham .
Written in English


Edition Notes

Thesis (Ph.D.) - University of Birmingham, Faculty of Science, School of Computer Science.

Statementby David Anthony Parker.
The Physical Object
Pagination194 p. ;
Number of Pages194
ID Numbers
Open LibraryOL21688745M

Symbolic model checking for purely probabilistic processes using MTBDDs [12] was introduced in [4] and further developed in [20, 3]. In this paper we consider models for concurrent probabilistic systems similar to those of [28, 7, 5] and the concurrent Markov chains of [35, 13], which extend the purely probabilistic processes through the addition. Numerous approaches to the symbolic implementation of probabilistic model checking and, more generally, the analysis of stochastic models, can be found in the literature; see [19] for a survey of this area. The main symbolic meth-ods applicable to stochastic models are Kronecker methods, matrix diagrams and MTBDD-based techniques. To date.

Probabilistic model checking Probabilistic Model Checker Probabilistic temporal logic specification send →P ¸ (deliver) 9 or 8 in a nutshell Probabilistic model The probability State 5: State 6: State 7: State 0 State or. Probabilistic model checking Probabilistic Model Checker Probabilistic temporal logic specification send →P ¸ (deliver) 9 or 8 The probability State 5: State 6: State 7: State 0 State or in a nutshell Probabilistic model

systems. For a variety of probabilistic systems, the most popular modeling formalism is Markov chain or Markov decision processes, for which Probabilistic Model Checking (PMC) tools such as PRISM [17] and MRMC [20] can be used. PMC is widely used and has been successfully applied to the verification of a range of timed and probabilistic systems. Motivated by the success of symbolic model checkers, such as SMV [55] which use BDDs (binary decision dia-grams) [15], we have developed a symbolic probabilistic model checker, PRISM [51,1]. In the non-probabilistic setting, model checking involves manipulation of state transition systems and sets of states, both of which can.


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Implementation of symbolic model checking for probabilistic systems by David Anthony Parker Download PDF EPUB FB2

Probabilistic model checking [14] is a generalization of model checking for verifying quantitative properties of systems which exhibit stochastic behavior, for example due to failures or.

In this thesis, we present efficient implementation techniques for probabilistic model checking, a method which can be used to analyse probabilistic systems such as randomised distributed algorithms, fault-tolerant processes and communication networks. Implementation of symbolic model checking for probabilistic systems Author: we present efficient implementation techniques for probabilistic model checking, a method which can be used to analyse probabilistic systems such as randomised distributed algorithms, fault-tolerant processes and communication networks.

and can automatically Cited by: Abstract. In this thesis, we present efficient implementation techniques for probabilistic model checking, a method which can be used to analyse probabilistic systems such as randomised distributed algorithms, fault-tolerant processes and communication networks.A probabilistic model checker inputs a probabilistic model and a specification, such as "the message will be delivered with.

IMPLEMENTATION OF SYMBOLIC MODEL CHECKING FOR PROBABILISTIC SYSTEMS. IMPLEMENTATION OF SYMBOLIC MODEL CHECKING FOR PROBABILISTIC SYSTEMS by DAVID ANTHONY PARKER.

A thesis submitted to the Faculty of Science of the University of Birmingham for the degree of Doctor of Philosophy. ProbVerus: Probabilistic Symbolic Model Checking 97 the necessity to solve huge systems of linear equations.

This paper proposes a novel approach; it presents an implementation of probabilistic model checking using multi terminal binary decision diagrams (MTBDDs) to perform the probability calculations.

PCTL model checking of symbolic probabilistic systems∗ Marta Kwiatkowska 1, Gethin Norman and Jeremy Sproston2 1 School of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom 2 Dipartimento di Informatica, Universita di Torino, Torino, Italy April 4, Abstract Probabilistic model checking is a method for automatically verifying.

ProbVerus is an implementation of probabilistic computation tree logic (PCTL) model checking using symbolic techniques. We present ProbVerus, demonstrate its use with a simple manufacturing. Based on, an MTBDD-based symbolic model checking procedure for purely probabilistic processes (state-labelled discrete Markov chains) for the logic PCTL of (a probabilistic variant of CTL) was first presented in, and since ex- tended to concurrent probabilistic systems in (without implementation).

a result, efficiency of probabilistic analysis lags behind efficient model checking tech- niques for conventionallogics, such as symbolic model checking [11, 12, 10, 8, 15, 28], for which tools capable of tackling industrial scale applications are available (). In this thesis, we present efficient implementation techniques for probabilistic model checking, a method which can be used to analyse probabilistic systems such as randomised distributed algorithms, fault-tolerant processes and communication networks.

In this thesis, we present ecient implementation techniques for probabilistic model checking, a method which can be used to analyse probabilistic systems such as randomised distributed algorithms, fault-tolerant processes and communication networks. Probabilistic model checking is a powerful technique for formally verifying quantitative properties of systems that exhibit stochastic behaviour.

Such systems are found in many application domains: for example, probabilistic behaviour may arise due to the presence of failures in unreliable hardware, message loss in wireless communication. In this thesis, we present efficient implementation techniques for probabilistic model checking, a method which can be used to analyse probabilistic systems such as randomised distributed algorithms, fault-tolerant processes and communication networks.

A probabilistic model checker inputs a probabilistic model and a specification, such as "the message will be delivered with probability 1. A probabilistic model checker inputs a probabilistic model and a specification, such as "the message will be delivered with probability 1", "the probability of shutdown occurring is at most " or "the probability of a leader being elected within 5 rounds is at least ", and can automatically verify if the specification is true in the : David Anthony Parker.

stochastic behavior of such systems: numerical and statistical. In the numerical approach, the formal model of the system is model checked for correctness with respect to the specification using symbolic and numerical methods. Model check-ers for different classes of stochastic processes and specification logics have been developed [8,14, Overview.

Probabilistic model checking is a formal technique for analysing systems that exhibit probabilistic behaviour. Examples include randomised algorithms, communication and security protocols, computer networks, biological signalling pathways, and many others. The course provides a detailed introduction to these techniques, covering both the underlying theory (Markov chains, Markov decision processes, temporal logics) and its practical application.

Probabilistic model checking is an automated veri cation method that aims to establish the correctness of probabilistic systems. Probability may arise, for example, due to failures of unreliable compo- nents, communication across lossy media, or through the use of randomi.

model checking procedure is proposed for the temporal case and implementation options are presented. Keywords: Probabilistic systems, model checking, digital circuits, temporal logic, probabilistic logic.

Perl Implementation: Analyzing PRISM Model Checker Output Supplementary material for ICFEM paper on Approximate Probabilistic Model Checking involving Unbounded Until Properties. Abstract: We study the problem of applying statistical methods for approximate model checking of probabilistic systems against properties encoded as PCTL formulas.

Multi-Terminal Binary Decision Diagrams (MTBDDs) have been successfully applied in symbolic model checking of probabilistic systems. In this paper we propose an encoding method for Probabilistic An MTBDD-Based Implementation of Forward Reachability for Probabilistic Timed Automata | SpringerLink.Probabilistic model checking • Significant overlap between implementation of probabilistic model checking for DTMCs, CTMCs, MDPs: − graph-based algorithms on underlying transition system • reachability, qualitative probabilistic reachability − numerical computation - calculation of .Probabilistic ModelChecking of Incomplete Models Shiraj Arora and M.

V. Panduranga Rao Indian Institute of Technology Hyderabad India {cs14resch, mvp}@ Abstract. It is crucial for accurate model checking that the model be a complete and faithful representation of the system. Unfortunately, this is.