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77
Symbolic model checking for probabilistic processes
 IN PROCEEDINGS OF ICALP '97
, 1997
"... We introduce a symbolic model checking procedure for Probabilistic Computation Tree Logic PCTL over labelled Markov chains as models. Model checking for probabilistic logics typically involves solving linear equation systems in order to ascertain the probability of a given formula holding in a stat ..."
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Cited by 83 (29 self)
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We introduce a symbolic model checking procedure for Probabilistic Computation Tree Logic PCTL over labelled Markov chains as models. Model checking for probabilistic logics typically involves solving linear equation systems in order to ascertain the probability of a given formula holding in a state. Our algorithm is based on the idea of representing the matrices used in the linear equation systems by MultiTerminal Binary Decision Diagrams (MTBDDs) introduced in Clarke et al [14]. Our procedure, based on the algorithm used by Hansson and Jonsson [24], uses BDDs to represent formulas and MTBDDs to represent Markov chains, and is efficient because it avoids explicit state space construction. A PCTL model checker is being implemented in Verus [9].
Quantitative Solution of OmegaRegular Games
"... We consider twoplayer games played for an infinite number of rounds, with ωregular winning conditions. The games may be concurrent, in that the players choose their moves simultaneously and independently, and probabilistic, in that the moves determine a probability distribution for the successor s ..."
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Cited by 44 (16 self)
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We consider twoplayer games played for an infinite number of rounds, with ωregular winning conditions. The games may be concurrent, in that the players choose their moves simultaneously and independently, and probabilistic, in that the moves determine a probability distribution for the successor state. We introduce quantitative game µcalculus, and we show that the maximal probability of winning such games can be expressed as the fixpoint formulas in this calculus. We develop the arguments both for deterministic and for probabilistic concurrent games; as a special case, we solve probabilistic turnbased games with ωregular winning conditions, which was also open. We also characterize the optimality, and the memory requirements, of the winning strategies. In particular, we show that while memoryless strategies suffice for winning games with safety and reachability conditions, Büchi conditions require the use of strategies with infinite memory. The existence of optimal strategies, as opposed to εoptimal, is only guaranteed in games with safety winning conditions.
pGCL: formal reasoning for random algorithms
, 1999
"... Dijkstra's guardedcommand language GCL contains explicit `demonic' nondeterminism, representing abstraction from (or ignorance of) which of two program fragments will be executed. We introduce probabilistic nondeterminism to the language, calling the result pGCL. Important is that both forms of non ..."
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Cited by 31 (8 self)
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Dijkstra's guardedcommand language GCL contains explicit `demonic' nondeterminism, representing abstraction from (or ignorance of) which of two program fragments will be executed. We introduce probabilistic nondeterminism to the language, calling the result pGCL. Important is that both forms of nondeterminism are present  both demonic and probabilistic: unlike earlier approaches, we do not deal only with one or the other. The programming logic of `weakest preconditions' for GCL becomes a logic of `greatest preexpectations' for pGCL: we embed predicates (Booleanvalued expressions over state variables) into arithmetic by writing [P ], an expression that is 1 when P holds and 0 when it does not. Thus in a trivial sense [P ] is the probability that P is true, and such embedded predicates are the basis for the more elaborate arithmetic expressions that we call "expectations". pGCL is suitable for describing random algorithms, at least over discrete distributions. In our presentation o...
An Operational Semantics for Probabilistic Concurrent Constraint Programming
, 1998
"... This paper investigates a probabilistic version of the concurrent constraint programming paradigm (CCP). The aim is to introduce the possibility to formulate so called "randomised algorithms" within the CCP framework. Differently from common approaches in (imperative) highlevel programming language ..."
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Cited by 31 (13 self)
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This paper investigates a probabilistic version of the concurrent constraint programming paradigm (CCP). The aim is to introduce the possibility to formulate so called "randomised algorithms" within the CCP framework. Differently from common approaches in (imperative) highlevel programming languages, which rely on some kind of random() function, we introduce randomness in the very definition of the language by means of a probabilistic choice construct. This allows a program to make stochastic moves during its execution. We call the resulting language Probabilistic Concurrent Constraint Programming (PCCP). We present an operational semantics for PCCP by means of a probabilistic transition system such that the execution of a PCCP program may be seen as a stochastic process, i.e. as a random walk on the transition graph. The transition probabilities are given explicitly. This semantics captures a notion of observables which combines results of computations and the probability of those re...
Abstract interpretation of probabilistic semantics
 In Seventh International Static Analysis Symposium (SAS’00), number 1824 in Lecture Notes in Computer Science
, 2000
"... Abstract. Following earlier models, we lift standard deterministic and nondeterministic semantics of imperative programs to probabilistic semantics. This semantics allows for random external inputs of known or unknown probability and random number generators. We then propose a method of analysis of ..."
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Cited by 29 (5 self)
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Abstract. Following earlier models, we lift standard deterministic and nondeterministic semantics of imperative programs to probabilistic semantics. This semantics allows for random external inputs of known or unknown probability and random number generators. We then propose a method of analysis of programs according to this semantics, in the general framework of abstract interpretation. This method lifts an “ordinary ” abstract lattice, for nonprobabilistic programs, to one suitable for probabilistic programs. Our construction is highly generic. We discuss the influence of certain parameters on the precision of the analysis, basing ourselves on experimental results. 1
Stochastic processes as concurrent constraint programs
 In Symposium on Principles of Programming Languages
, 1999
"... ) Vineet Gupta Radha Jagadeesan Prakash Panangaden y vgupta@mail.arc.nasa.gov radha@cs.luc.edu prakash@cs.mcgill.ca Caelum Research Corporation Dept. of Math. and Computer Sciences School of Computer Science NASA Ames Research Center Loyola UniversityLake Shore Campus McGill University Moffe ..."
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Cited by 29 (1 self)
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) Vineet Gupta Radha Jagadeesan Prakash Panangaden y vgupta@mail.arc.nasa.gov radha@cs.luc.edu prakash@cs.mcgill.ca Caelum Research Corporation Dept. of Math. and Computer Sciences School of Computer Science NASA Ames Research Center Loyola UniversityLake Shore Campus McGill University Moffett Field CA 94035, USA Chicago IL 60626, USA Montreal, Quebec, Canada Abstract This paper describes a stochastic concurrent constraint language for the description and programming of concurrent probabilistic systems. The language can be viewed both as a calculus for describing and reasoning about stochastic processes and as an executable language for simulating stochastic processes. In this language programs encode probability distributions over (potentially infinite) sets of objects. We illustrate the subtleties that arise from the interaction of constraints, random choice and recursion. We describe operational semantics of these programs (programs are run by sampling random choices), deno...
Decision Algorithms for Probabilistic Bisimulation
, 2002
"... We propose decision algorithms for bisimulation relations de ned on probabilistic automata, a model for concurrent nondeterministic systems with randomization. The algorithms decide both strong and weak bisimulation relations based on deterministic as well as randomized schedulers. These algori ..."
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Cited by 22 (3 self)
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We propose decision algorithms for bisimulation relations de ned on probabilistic automata, a model for concurrent nondeterministic systems with randomization. The algorithms decide both strong and weak bisimulation relations based on deterministic as well as randomized schedulers. These algorithms extend and complete other known algorithms for simpler relations and models. The algorithm we present for strong probabilistic bisimulation has polynomial time complexity, while the algorithm for weak probabilistic bisimulation is exponential; however we argue that the latter is feasible in practice.
Probabilistic Concurrent Constraint Programming
 In Proceedings of CONCUR 97
, 1997
"... . We extend cc to allow the specification of a discrete probability distribution for random variables. We demonstrate the expressiveness of pcc by synthesizing combinators for default reasoning. We extend pcc uniformly over time, to get a synchronous reactive probabilistic programming language, Time ..."
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Cited by 21 (0 self)
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. We extend cc to allow the specification of a discrete probability distribution for random variables. We demonstrate the expressiveness of pcc by synthesizing combinators for default reasoning. We extend pcc uniformly over time, to get a synchronous reactive probabilistic programming language, Timed pcc. We describe operational and denotational models for pcc (and Timed pcc). The key feature of the denotational model(s) is that parallel composition is essentially set intersection. We show that the denotational model of pcc (resp. Timed pcc) is conservative over cc (resp. tcc). We also show that the denotational models are fully abstract for an operational semantics that records probability information. 1 Introduction Concurrent constraint programming(CCP, [Sar93]) is an approach to computation which uses constraints for the compositional specification of concurrent systems. It replaces the traditional notion of a store as a valuation of variables with the notion of a store as a cons...
Concurrent Constraint Programming: Towards Probabilistic Abstract Interpretation
 Proc. of the 23rd International Symposium on Mathematical Foundations of Computer Science, MFCS'98, Lecture Notes in Computer Science
, 2000
"... We present a method for approximating the semantics of probabilistic programs to the purpose of constructing semanticsbased analyses of such programs. The method resembles the one based on Galois connection as developed in the Cousot framework for abstract interpretation. The main difference betwee ..."
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Cited by 20 (8 self)
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We present a method for approximating the semantics of probabilistic programs to the purpose of constructing semanticsbased analyses of such programs. The method resembles the one based on Galois connection as developed in the Cousot framework for abstract interpretation. The main difference between our approach and the standard theory of abstract interpretation is the choice of linear space structures instead of ordertheoretic ones as semantical (concrete and abstract) domains. We show that our method generates "best approximations" according to an appropriate notion of precision defined in terms of a norm. Moreover, if recasted in a ordertheoretic setting these approximations are correct in the sense of classical abstract interpretation theory. We use Concurrent ...