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An abstract interpretationbased refinement algorithm for strong preservation
 In Proc. 11th Intern. Conf. on Tools and Algorithms for the Construction and Analysis of Systems (TACAS’05), LNCS 3440
, 2005
"... Abstract. The Paige and Tarjan algorithm (PT) for computing the coarsest refinement of a state partition which is a bisimulation on some Kripke structure is well known. It is also well known in abstract model checking that bisimulation is equivalent to strong preservation of CTL and in particular of ..."
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Cited by 15 (7 self)
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Abstract. The Paige and Tarjan algorithm (PT) for computing the coarsest refinement of a state partition which is a bisimulation on some Kripke structure is well known. It is also well known in abstract model checking that bisimulation is equivalent to strong preservation of CTL and in particular of HennessyMilner logic. Building on these facts, we analyze the basic steps of the PT algorithm from an abstract interpretation perspective, which allows us to reason on strong preservation in the context of generic inductively defined (temporal) languages and of abstract models specified by abstract interpretation. This leads us to design a generalized PaigeTarjan algorithm, called GPT, for computing the minimal refinement of an abstract interpretationbased model that strongly preserves some given language. It turns out that PT can be obtained by instantiating GPT to the domain of state partitions for the case of strong preservation of HennessyMilner logic. We provide a number of examples showing that GPT is of general use. We show how two wellknown efficient algorithms for computing simulation and stuttering equivalence can be viewed as simple instances of GPT. Moreover, we instantiate GPT in order to design a O(TransitionsStates)time algorithm for computing the coarsest refinement of a given partition that strongly preserves the language generated by the reachability operator EF. 1
Behavior composition in the presence of failure
 In Proc. of KR 2008
"... In this paper we articulate theoretical bases for robust behavior composition of multiple modules (e.g., agents, devices, etc.) by relying on the formal notion of simulation. Specifically, we consider the problem of synthesizing a fully controllable target behavior from a library of available partia ..."
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Cited by 14 (7 self)
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In this paper we articulate theoretical bases for robust behavior composition of multiple modules (e.g., agents, devices, etc.) by relying on the formal notion of simulation. Specifically, we consider the problem of synthesizing a fully controllable target behavior from a library of available partially controllable behaviors that are to execute within a shared, fully observable, but partially predictable, environment. Both behaviors and environment are represented as finite state transition systems. While previous solutions to this problem assumed full reliability, here we consider unforeseen potential failures, such as a module, or the environment, unexpectedly changing its state, or a module becoming temporarily unavailable or dropping out permanently. Based on the notion of simulation, we propose an alternative synthesis approach that allows for refining the solution at hand, either onthefly or parsimoniously, so as to cope with failures. Interestingly, it turns out that the proposed simulationbased technique is computationally an improvement over previously known methods that assumed fullreliability.
Generalized strong preservation by abstract interpretation
 J. Logic and Computation
, 2007
"... Standard abstract model checking relies on abstract Kripke structures which approximate concrete models by gluing together indistinguishable states, namely by a partition of the concrete state space. models that are more general than abstract Kripke structures. Accordingly, strong preservation is ge ..."
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Cited by 11 (8 self)
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Standard abstract model checking relies on abstract Kripke structures which approximate concrete models by gluing together indistinguishable states, namely by a partition of the concrete state space. models that are more general than abstract Kripke structures. Accordingly, strong preservation is generalized to abstract interpretationbased models and precisely related to the concept of completeness in abstract interpretation. The problem of minimally refining an abstract model in order to make it strongly preserving for some language L can be formulated as a minimal domain refinement in abstract interpretation in order to get completeness w.r.t. the logical/temporal operators of L. It turns out that this refined strongly preserving abstract model always exists and can be characterized as a greatest fixed point. As a consequence, some wellknown behavioural equivalences, like bisimulation, simulation and stuttering, and their corresponding partition refinement algorithms can be elegantly characterized in abstract interpretation as completeness properties and refinements.
AUTOMATIC SERVICE COMPOSITION VIA SIMULATION
 INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE
"... In this paper we study the issue of service composition, for services that export a representation of their behavior in the form of a finite deterministic transition system. In particular, given a specification of the target service requested by the client as a finite deterministic transition syste ..."
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Cited by 10 (4 self)
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In this paper we study the issue of service composition, for services that export a representation of their behavior in the form of a finite deterministic transition system. In particular, given a specification of the target service requested by the client as a finite deterministic transition system, the problem we face is how we can exploit the computations of the available services for realizing the computations of the target service. While ways to tackle such a problem are known, in this paper we present a new technique that is based on the notion of simulation, which is still optimal from the computational complexity point. Notably, such a technique, opens up the possibility of devising composition in a “justintime” fashion. Indeed, we show that, by exploiting simulation, it is actually possible to implicitly compute all possible compositions at once, and delay the choice of the actual composition to runtime.
An Efficient Simulation Algorithm based on Abstract Interpretation
, 709
"... A number of algorithms for computing the simulation preorder are available. Let Σ denote the state space, → the transition relation and Psim the partition of Σ induced by simulation equivalence. The algorithms by Henzinger, Henzinger, Kopke and by Bloom and Paige run in O(Σ→)time and, as far a ..."
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Cited by 5 (2 self)
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A number of algorithms for computing the simulation preorder are available. Let Σ denote the state space, → the transition relation and Psim the partition of Σ induced by simulation equivalence. The algorithms by Henzinger, Henzinger, Kopke and by Bloom and Paige run in O(Σ→)time and, as far as timecomplexity is concerned, they are the best available algorithms. However, these algorithms have the drawback of a space complexity that is more than quadratic in the size of the state space. The algorithm by Gentilini, Piazza, Policriti — subsequently corrected by van Glabbeek and Ploeger — appears to provide the best compromise between time and space complexity. Gentilini et al.’s algorithm runs in O(Psim  2 →)time while the space complexity is in O(Psim  2 + Σ  log Psim). We present here a new efficient simulation algorithm that is obtained as a modification of Henzinger et al.’s algorithm and whose correctness is based on some techniques used in applications of abstract interpretation to model checking. Our algorithm runs in O(Psim→)time and O(PsimΣ  log Σ)space. Thus, this algorithm improves the best known time bound while retaining an acceptable space complexity that is in general less than quadratic in the size of the state space. An experimental evaluation showed good comparative results with respect to Henzinger, Henzinger and Kopke’s algorithm. 1
Saving Space in a Time Efficient Simulation Algorithm
"... A number of algorithms are available for computing the simulation relation on Kripke structures and on labelled transition systems representing concurrent systems. Among them, the algorithm by Ranzato and Tapparo [2007] has the best time complexity, while the algorithm by Gentilini et al. [2003] – ..."
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Cited by 4 (0 self)
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A number of algorithms are available for computing the simulation relation on Kripke structures and on labelled transition systems representing concurrent systems. Among them, the algorithm by Ranzato and Tapparo [2007] has the best time complexity, while the algorithm by Gentilini et al. [2003] – successively corrected by van Glabbeek and Ploeger [2008] – has the best space complexity. Both space and time complexities are critical issues in a simulation algorithm, in particular memory requirements are crucial in the context of model checking when dealing with large state spaces. We propose here a new simulation algorithm that is obtained as a space saving modification of the time efficient algorithm by Ranzato and Tapparo: a symbolic representation of sets is embedded in this algorithm so that any set of states manipulated by the algorithm can be efficiently stored as a set of blocks of a suitable state partition. It turns out that this new simulation algorithm retains a space complexity comparable with Gentilini et al.’s algorithm while improving on Gentilini et al.’s time bound. 1.
Initial Draft of a Language Syntax
 REWERSE, Deliverable I4D6, 2006. [Online]. Available: http://rewerse.net/ deliverables/m18/i4d6.pdf
, 2006
"... This article defines an initial proposal for the syntax of the I4 query language, Xcerpt. Indeed, not only a single syntax, but rather three syntactical forms of Xcerpt are introduced: (1) the term syntax, a nonstandard syntax that allows the succinct formulation of queries and is intended mostly f ..."
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Cited by 3 (1 self)
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This article defines an initial proposal for the syntax of the I4 query language, Xcerpt. Indeed, not only a single syntax, but rather three syntactical forms of Xcerpt are introduced: (1) the term syntax, a nonstandard syntax that allows the succinct formulation of queries and is intended mostly for human authors; (2) the XML syntax provides a fine granular language markup in XML, ideal for processing through XMLbased tools and for automated query generation or reasoning about query programs; (3) the compact XML syntax is a hybrid syntax of (1) and (2). The concepts are introduced UML. In addition to the formal syntax specification, principles of the syntax design are disucssed. Furthermore, for a number of advanced constructs the reasoning supporting the design choice, as well as alternative solutions are illustrated. An impression of how the introduced language constructs allow to write and understand complex queries is given by numerous examples interspersed among the construct specifications.
Generalizing the PaigeTarjan Algorithm by Abstract Interpretation
"... The Paige and Tarjan algorithm (PT) for computing the coarsest refinement of a state partition which is a bisimulation on some Kripke structure is well known. It is also well known in model checking that bisimulation is equivalent to strong preservation of CTL or, equivalently, of HennessyMilner lo ..."
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Cited by 2 (2 self)
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The Paige and Tarjan algorithm (PT) for computing the coarsest refinement of a state partition which is a bisimulation on some Kripke structure is well known. It is also well known in model checking that bisimulation is equivalent to strong preservation of CTL or, equivalently, of HennessyMilner logic. Drawing on these observations, we analyze the basic steps of the PT algorithm from an abstract interpretation perspective, which allows us to reason on strong preservation in the context of arbitrary (temporal) languages and of generic abstract models, possibly different from standard state partitions, specified by abstract interpretation. This leads us to design a generalized PaigeTarjan algorithm, called GPT, for computing the minimal refinement of an abstract interpretationbased model that strongly preserves some given language. It turns out that PT is a straight instance of GPT on the domain of state partitions for the case of strong preservation of HennessyMilner logic. We provide a number of examples showing that GPT is of general use. We first show how a wellknown efficient algorithm for computing stuttering equivalence can be viewed as a simple instance of GPT. We then instantiate GPT in order to design a new efficient algorithm for computing simulation equivalence that is competitive with the best available algorithms. Finally, we show how GPT allows to deal with strong preservation of new languages by providing an efficient algorithm that computes the coarsest refinement of a given partition that strongly preserves a language generated by the reachability operator. 1
Static Analysis, Abstract Interpretation and Verification in (Constraint Logic) Programming
"... interpretation and verification. Operational and denotational semantics of logic programs feature simple and clean inductive definitions that made it possible to apply a variety of known analysis and verification techniques and tools and to define new ones ..."
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Cited by 1 (0 self)
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interpretation and verification. Operational and denotational semantics of logic programs feature simple and clean inductive definitions that made it possible to apply a variety of known analysis and verification techniques and tools and to define new ones
Three Simulation Algorithms for Labelled Transition Systems
, 2013
"... Algorithms which compute the coarsest simulation preorder are generally designed on Kripke structures. Only in a second time they are extended to labelled transition systems. By doing this, the size of the alphabet appears in general as a multiplicative factor to both time and space complexities. Le ..."
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Cited by 1 (1 self)
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Algorithms which compute the coarsest simulation preorder are generally designed on Kripke structures. Only in a second time they are extended to labelled transition systems. By doing this, the size of the alphabet appears in general as a multiplicative factor to both time and space complexities. Let Q denotes the state space, →thetransition relation, ΣthealphabetandPsim thepartition ofQinduced by the coarsest simulation equivalence. In this paper, we propose a base algorithm which minimizes, since the first stages of its design, the incidence of the size of the alphabet in both time and space complexities. This base algorithm, inspired by the one of Paige and Tarjan in 1987 for bisimulation and the one of Ranzato and Tapparo in 2010 for simulation, is then derived in three versions. One of them has the best bit space complexity up to now, O(Psim  2 +→.log→), while another one has the best time complexity up to now, O(Psim.→). Note the absence of the alphabet in these complexities. A third version happens to be a nice compromise between space and time since it runs in O(b.Psim.→) time, with b a branching factor generally far below Psim, and uses O(Psim  2.logPsim+→.log→) bits. 1