T(O)RMC: A Tool for (ω)-Regular Model Checking

Regular model checking is a generic technique for verification of infinite-state and/or parametrised systems which uses finite word automata or finite tree automata to finitely represent potentially infinite sets of reachable configurations of the systems being verified. The problems addressed by regular model checking are typically undecidable. In order to facilitate termination in as many cases as possible, acceleration is needed in the incremental computation of the set of reachable configurations in regular model checking. In this work, we describe how various incrementally refinable abstractions on finite (word and tree) automata can be used for this purpose. Moreover, the use of abstraction does not only increase chances of the technique to terminate, but it also significantly reduces the problem of an explosion in the number of states of the automata that are generated by regular model checking. We illustrate the efficiency of abstract regular (tree) model checking in verification of simple systems with various sources of infinity such as unbounded counters, queues, stacks, and parameters. We then show how abstract regular tree model checking can be used for verification of programs manipulating tree-like dynamic data structures. Even more complex data structures can be handled using a suitable tree-like encoding.

Regular model checking is a form of symbolic model checking technique for systems whose states can be represented as finite words over a finite alphabet, where regular sets are used as symbolic representation. A major problem in symbolic model checking of parameterized and infinite-state systems is that fixpoint computations to generate the set of reachable states or the set of reachable loops do not terminate in general. Therefore, acceleration techniques have been developed, which calculate the effect of arbitrarily long sequences of transitions generated by some action. We present a systematic method for using acceleration in regular model checking, for the case where each transition changes at most one position in the word; this includes many parameterized algorithms and algorithms on data structures. The method extracts a maximal (in a certain sense) set of actions from a transition relation. These actions, and systematically obtained compositions of them, are accelerated to speed up a fixpoint computation. The extraction can be done on any representation of the transition relation, e.g., as a union of actions or as a single monolithic transducer. Using this approach, we are for the first time able to verify completely automatically both safety and absence of starvation properties for a collection of parameterized synchronization protocols from the literature; for some protocols, we obtain significant improvements in verification time. The results show that symbolic state-space exploration, without using abstractions, is a viable alternative for verification of parameterized systems with a linear topology.KeywordsModel CheckTransition RelationReachable StateLiveness PropertySymbolic Model CheckThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We propose abstract regular model checking as a new generic technique for verification of parametric and infinite-state systems. The technique combines the two approaches of regular model checking and verification by abstraction. We propose a general framework of the method as well as several concrete ways of abstracting automata or transducers, which we use for modelling systems and encoding sets of their configurations as usual in regular model checking. The abstraction is based on collapsing states of automata (or transducers) and its precision is being incrementally adjusted by analysing spurious counterexamples. We illustrate the technique on verification of a wide range of systems including a novel application of automata-based techniques to an example of systems with dynamic linked data structures.

Tree regular model checking: A simulation-based approach

“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.

Regular model checking is a technique for the verification of infinite-state systems whose configurations can be represented as finite words over a suitable alphabet. The form we are studying applies to systems whose set of initial configurations is regular, and whose transition relation is captured by a length-preserving transducer. To verify safety properties, regular model checking iteratively computes automata recognizing increasingly larger regular sets of reachable configurations, and checks if they contain unsafe configurations. Since this procedure often does not terminate, acceleration, abstraction, and widening techniques have been developed to compute a regular superset of the reachable configurations. In this paper, we develop a complementary procedure. Instead of approaching the set of reachable configurations from below, we start with the set of all configurations and approach it from above. We use that the set of reachable configurations is equal to the intersection of all inductive invariants of the system. Since this intersection is non-regular in general, we introduce b-invariants, defined as those representable by CNF-formulas with at most b clauses. We prove that, for every $b\geq0$, the intersection of all inductive b-invariants is regular, and we construct an automaton recognizing it. We show that whether this automaton accepts some unsafe configuration is in EXPSPACE for every $b\geq0$, and PSPACE-complete for b=1. Finally, we study how large must b be to prove safety properties of a number of benchmarks.

We consider the framework of regular tree model checking where sets of configurations of a system are represented by regular tree languages and its dynamics is modeled by a term rewriting system (or a regular tree transducer). We focus on the computation of the reachability set R*(L) where R is a regular tree transducer and L is a regular tree language. The construction of this set is not possible in general. Therefore, we present a general acceleration technique, called regular tree widening which allows to speed up the convergence of iterative fixpoint computations in regular tree model checking. This technique can be applied uniformly to various kinds of transformations. We show the application of our framework to different analysis contexts: verification of parameterized tree networks and data-flow analysis of multithreaded programs. Parametrized networks are modeled by relabeling tree transducers, and multithreaded programs are modeled by term rewriting rules encoding transformations on control structures. We prove that our widening technique can emulate many existing algorithms for special classes of transformations and we show that it can deal with transformations beyond the scope of these algorithms.

We revisit the classic problem of proving safety over parameterised concurrent systems, i.e., an infinite family of finite-state concurrent systems that are represented by some finite (symbolic) means. An example of such an infinite family is a dining philosopher protocol with any number n of processes (n being the parameter that defines the infinite family). Regular model checking is a well-known generic framework for modelling parameterised concurrent systems, where an infinite set of configurations (resp. transitions) is represented by a regular set (resp. regular transducer). Although verifying safety properties in the regular model checking framework is undecidable in general, many sophisticated semi-algorithms have been developed in the past fifteen years that can successfully prove safety in many practical instances. In this paper, we propose a simple solution to synthesise regular inductive invariants that makes use of Angluin's classic L∗ algorithm (and its variants). We provide a termination guarantee when the set of configurations reachable from a given set of initial configurations is regular. We have tested L∗ algorithm on standard (as well as new) examples in regular model checking including the dining philosopher protocol, the dining cryptographer protocol, and several mutual exclusion protocols (e.g. Bakery, Burns, Szymanski, and German). Our experiments show that, despite the simplicity of our solution, it can perform at least as well as existing semi-algorithms.

Extracting counterexamples induced by safety violation in linear hybrid systems

Model Predictive Control (MPC), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book. With a simple, unified approach, and with attention to real-time implementation, it covers predictive control theory including the stability, feasibility, and robustness of MPC controllers. The theory of explicit MPC, where the nonlinear optimal feedback controller can be calculated efficiently, is presented in the context of linear systems with linear constraints, switched linear systems, and, more generally, linear hybrid systems. Drawing upon years of practical experience and using numerous examples and illustrative applications, the authors discuss the techniques required to design predictive control laws, including algorithms for polyhedral manipulations, mathematical and multiparametric programming and how to validate the theoretical properties and to implement predictive control policies. The most important algorithms feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.

Model Predictive Control (MPC), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book. With a simple, unified approach, and with attention to real-time implementation, it covers predictive control theory including the stability, feasibility, and robustness of MPC controllers. The theory of explicit MPC, where the nonlinear optimal feedback controller can be calculated efficiently, is presented in the context of linear systems with linear constraints, switched linear systems, and, more generally, linear hybrid systems. Drawing upon years of practical experience and using numerous examples and illustrative applications, the authors discuss the techniques required to design predictive control laws, including algorithms for polyhedral manipulations, mathematical and multiparametric programming and how to validate the theoretical properties and to implement predictive control policies. The most important algorithms feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.

In this book we focused on established decidability results in parameterized model checking. As this topic is a lively field, we cannot make any claim about completeness of our survey. In particular, we left out the large body of research that includes invariant-based techniques, regular model checking, symbolic methods, techniques for counter automata, and abstraction-based methods. In this chapter we give a brief overview of these methods and give further references to the interested reader. Our classification of the techniques is unavoidably subjective, as except for several cornerstone papers, many techniques adopted multiple different ideas.

In reinforcement learning, an agent incrementally refines a behavioral policy through a series of episodic interactions with its environment. This process can be characterized as explicit reinforcement learning, as it deals with explicit states and concrete transitions. Building upon the concept of symbolic model checking, we propose a symbolic variant of reinforcement learning, in which sets of states are represented through predicates and transitions are represented by predicate transformers. Drawing inspiration from regular model checking, we choose regular languages over the states as our predicates, and rational transductions as predicate transformations. We refer to this framework as regular reinforcement learning, and study its utility as a symbolic approach to reinforcement learning. Theoretically, we establish results around decidability, approximability, and efficient learnability in the context of regular reinforcement learning. Towards practical applications, we develop a deep regular reinforcement learning algorithm, enabled by the use of graph neural networks. We showcase the applicability and effectiveness of (deep) regular reinforcement learning through empirical evaluation on a diverse set of case studies.

The stability of linear time-delay systems with point internal delays is difficult to deal with in practice due to the fact that their characteristic equation is usually of transcendent type rather than of polynomial type. Stability tests for this class of systems are obtained either based on extensions of classical tests applicable to delay-free systems or on approaches within the framework of two-dimensional digital filters. Some of those two-dimensional stability tests are also proved to be useful for stability testing of a common class of linear hybrid systems.

This paper is concerned with the exact calculus of the switching times for linear time invariant hybrid systems. We study the system's behavior that incorporates dynamical switches from a current location to another and propose an explicit procedure for a constructive treatment of the switching times. An implementable computational method elaborated in this paper makes it possible to solve the multidimensional problem of switching times detections in an algorithmic form. In this sense, the above numerical procedure also determines the sequence of locations of the hybrid system. Finally, we apply the proposed constructive approach to two illustrative examples.