1,735 research outputs found
Improving BDD Based Symbolic Model Checking with Isomorphism Exploiting Transition Relations
Symbolic model checking by using BDDs has greatly improved the applicability
of model checking. Nevertheless, BDD based symbolic model checking can still be
very memory and time consuming. One main reason is the complex transition
relation of systems. Sometimes, it is even not possible to generate the
transition relation, due to its exhaustive memory requirements. To diminish
this problem, the use of partitioned transition relations has been proposed.
However, there are still systems which can not be verified at all. Furthermore,
if the granularity of the partitions is too fine, the time required for
verification may increase. In this paper we target the symbolic verification of
asynchronous concurrent systems. For such systems we present an approach which
uses similarities in the transition relation to get further memory reductions
and runtime improvements. By applying our approach, even the verification of
systems with an previously intractable transition relation becomes feasible.Comment: In Proceedings GandALF 2011, arXiv:1106.081
Enhancing Approximations for Regular Reachability Analysis
This paper introduces two mechanisms for computing over-approximations of
sets of reachable states, with the aim of ensuring termination of state-space
exploration. The first mechanism consists in over-approximating the automata
representing reachable sets by merging some of their states with respect to
simple syntactic criteria, or a combination of such criteria. The second
approximation mechanism consists in manipulating an auxiliary automaton when
applying a transducer representing the transition relation to an automaton
encoding the initial states. In addition, for the second mechanism we propose a
new approach to refine the approximations depending on a property of interest.
The proposals are evaluated on examples of mutual exclusion protocols
Symbolic Algorithms for Language Equivalence and Kleene Algebra with Tests
We first propose algorithms for checking language equivalence of finite
automata over a large alphabet. We use symbolic automata, where the transition
function is compactly represented using a (multi-terminal) binary decision
diagrams (BDD). The key idea consists in computing a bisimulation by exploring
reachable pairs symbolically, so as to avoid redundancies. This idea can be
combined with already existing optimisations, and we show in particular a nice
integration with the disjoint sets forest data-structure from Hopcroft and
Karp's standard algorithm. Then we consider Kleene algebra with tests (KAT), an
algebraic theory that can be used for verification in various domains ranging
from compiler optimisation to network programming analysis. This theory is
decidable by reduction to language equivalence of automata on guarded strings,
a particular kind of automata that have exponentially large alphabets. We
propose several methods allowing to construct symbolic automata out of KAT
expressions, based either on Brzozowski's derivatives or standard automata
constructions. All in all, this results in efficient algorithms for deciding
equivalence of KAT expressions
Efficient Symmetry Reduction and the Use of State Symmetries for Symbolic Model Checking
One technique to reduce the state-space explosion problem in temporal logic
model checking is symmetry reduction. The combination of symmetry reduction and
symbolic model checking by using BDDs suffered a long time from the
prohibitively large BDD for the orbit relation. Dynamic symmetry reduction
calculates representatives of equivalence classes of states dynamically and
thus avoids the construction of the orbit relation. In this paper, we present a
new efficient model checking algorithm based on dynamic symmetry reduction. Our
experiments show that the algorithm is very fast and allows the verification of
larger systems. We additionally implemented the use of state symmetries for
symbolic symmetry reduction. To our knowledge we are the first who investigated
state symmetries in combination with BDD based symbolic model checking
Bounded Situation Calculus Action Theories
In this paper, we investigate bounded action theories in the situation
calculus. A bounded action theory is one which entails that, in every
situation, the number of object tuples in the extension of fluents is bounded
by a given constant, although such extensions are in general different across
the infinitely many situations. We argue that such theories are common in
applications, either because facts do not persist indefinitely or because the
agent eventually forgets some facts, as new ones are learnt. We discuss various
classes of bounded action theories. Then we show that verification of a
powerful first-order variant of the mu-calculus is decidable for such theories.
Notably, this variant supports a controlled form of quantification across
situations. We also show that through verification, we can actually check
whether an arbitrary action theory maintains boundedness.Comment: 51 page
Towards efficient verification of systems with dynamic process creation
Modelling and analysis of dynamic multi-threaded state systems often encounters obstacles when one wants to use automated verification methods, such as model checking. Our aim in this paper is to develop a technical device for coping with one such obstacle, namely that caused by dynamic process creation. We first introduce a general class of coloured Petri nets-not tied to any particular syntax or approach-allowing one to capture systems with dynamic (and concurrent) process creation as well as capable of manipulating data. Following this, we introduce the central notion of our method which is a marking equivalence that can be efficiently computed and then used, for instance, to aggregate markings in a reachability graph. In some situations, such an aggregation may produce a finite representation of an infinite state system which still allows one to establish the relevant behavioural properties. We show feasibility of the method on an example and provide initial experimental results
Omega-Regular Model Checking
peer reviewed"Regular model checking" is the name of a family of techniques for analyzing infinite-state systems in which states are represented by words or trees, sets of states by finite automata on these objects, and transitions by finite automata operating on pairs of state encodings, i.e. finite-state transducers. In this context, the central problem is then to compute the iterative closure of a finite-state transducer. This paper addresses the use of regular model-checking like techniques for systems whose states are represented by infinite (omega) words. Its main motivation is to show the feasibility and usefulness of this approach through a combination of the necessary theoretical developments, implementation, and experimentation. The iteration technique that is used is adapted from recent work of the authors on the iteration of finite-word transducers. It proceeds by comparing successive elements of a sequence of approximations of the iteration, detecting an "increment" that is added to move from one approximation to the next, and extrapolating the sequence by allowing arbitrary repetitions of this increment. By restricting oneself to weak deterministic Buchi automata, and using a number of implementation optimizations, examples of significant size can be handled. The proposed transducer iteration technique can just as well be exploited to compute the closure of a given set of states by the transducer iteration, which has proven to be a very effective way of using the technique. Examples such as a leaking gas burner in which time is modeled by real variables have been handled completely within the automata-theoretic setting
Induction of Interpretable Possibilistic Logic Theories from Relational Data
The field of Statistical Relational Learning (SRL) is concerned with learning
probabilistic models from relational data. Learned SRL models are typically
represented using some kind of weighted logical formulas, which make them
considerably more interpretable than those obtained by e.g. neural networks. In
practice, however, these models are often still difficult to interpret
correctly, as they can contain many formulas that interact in non-trivial ways
and weights do not always have an intuitive meaning. To address this, we
propose a new SRL method which uses possibilistic logic to encode relational
models. Learned models are then essentially stratified classical theories,
which explicitly encode what can be derived with a given level of certainty.
Compared to Markov Logic Networks (MLNs), our method is faster and produces
considerably more interpretable models.Comment: Longer version of a paper appearing in IJCAI 201
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