Richard Mayr

Eager markov chains

We consider infinite-state discrete Markov chains which are eager: the probability of avoiding a defined set of final states for more than n steps is bounded by some exponentially decreasing function f(n). We prove that eager Markov chains include those induced by Probabilistic Lossy Channel Systems, Probabilistic Vector Addition Systems with States, and Noisy Turing Machines, and that the bounding function f(n) can be effectively constructed for them. Furthermore, we study the problem of computing the expected reward (or cost) of runs until reaching the final states, where rewards are assigned to individual runs by computable reward functions. For eager Markov chains, an effective path exploration scheme, based on forward reachability analysis, can be used to approximate the expected reward up-to an arbitrarily small error.

On the verification of broadcast protocols

We analyze the model-checking problems for safety and liveness properties in parameterized broadcast protocols. We show that the procedure suggested previously for safety properties may not terminate, whereas termination is guaranteed for the procedure based on upward closed sets. We show that the model-checking problem for liveness properties is undecidable. In fact, even the problem of deciding if a broadcast protocol may exhibit an infinite behavior is undecidable.

Process rewrite systems

Many formal models for infinite-state concurrent systems are equivalent to special classes of rewrite systems. We classify these models by their expressiveness and define a hierarchy of classes of rewrite systems. We show that this hierarchy is strict with respect to bisimulation equivalence. The most general and most expressive class of systems in this hierarchy is called process rewrite systems (PRS). They subsume Petri nets, PA-processes, and pushdown processes and are strictly more expressive than any of these. Intuitively, PRS can be seen as an extension of Petri nets by subroutines that can return a value to their caller. We show that the reachability problem is decidable for PRS. It is even decidable if there is a reachable state that satisfies certain properties that can be encoded in a simple logic. Thus, PRS are more expressive than Petri nets, but not Turing-powerful.

Model checking probabilistic pushdown automata

We consider the model checking problem for probabilistic pushdown automata (pPDA) and properties expressible in various probabilistic logics. We start with properties that can be formulated as instances of a generalized random walk problem. We prove that both qualitative and quantitative model checking for this class of properties and pPDA is decidable. Then, we show that model checking for the qualitative fragment of the logic PCTL and pPDA is also decidable. Moreover, we develop an error-tolerant model checking algorithm for general PCTL and the subclass of stateless pPDA. Finally, we consider the class of properties definable by deterministic Buchi automata, and show that both qualitative and quantitative model checking for pPDA is decidable.

Higher-order rewrite systems and their confluence

Abstract We study higher-order rewrite systems (HRSs) which extend term rewriting to λ-terms. HRSs can describe computations over terms with bound variables. We show that rewriting with HRSs is closely related to undirected equational reasoning. We define pattern rewrite systems (PRSs) as a special case of HRSs and extend three confluence results from term rewriting to PRSs: the critical pair lemma by Knuth and Bendix, confluence of rewriting modulo equations a la Huet, and confluence of orthogonal PRSs.

Advanced Ramsey-based Büchi automata inclusion testing

Checking language inclusion between two nondeterministic Buchi automata A and B is computationally hard (PSPACE-complete). However, several approaches which are efficient in many practical cases have been proposed. We build on one of these, which is known as the Ramsey-based approach. It has recently been shown that the basic Ramsey-based approach can be drastically optimized by using powerful subsumption techniques, which allow one to prune the search-space when looking for counterexamples to inclusion. While previous works only used subsumption based on set inclusion or forward simulation on A and B, we propose the following new techniques: (1) A larger subsumption relation based on a combination of backward and forward simulations on A and B. (2) A method to additionally use forward simulation between A and B. (3) Abstraction techniques that can speed up the computation and lead to early detection of counterexamples. The new algorithm was implemented and tested on automata derived from real-world model checking benchmarks, and on the Tabakov-Vardi random model, thus showing the usefulness of the proposed techniques.

Undecidable problems in unreliable computations

Lossy counter machines are defined as Minsky counter machines where the values in the counters can spontaneously decrease at any time. While termination is decidable for lossy counter machines, structural termination (termination for every input) is undecidable. This undecidability result has far-reaching consequences. Lossy counter machines can be used as a general tool to prove the undecidability of many problems, for example: (1) The verification of systems that model communication through unreliable channels (e.g., model checking lossy fifo-channel systems and lossy vector addition systems). (2) Several problems for reset Petri nets, like structural termination, boundedness and structural boundedness. (3) Parameterized problems like fairness of broadcast communication protocols.

Model checking lossy vector addition systems

Lossy VASS (vector addition systems with states) are defined as a subclass of VASS in analogy to lossy FIFO-channel systems. They can be used to model concurrent systems with unreliable communication. We analyze the decidability of model checking problems for lossy systems and several branching-time and linear-time temporal logics. We present an almost complete picture of the decidability of model checking for normal VASS, lossy VASS and lossy VASS with test for zero.

Quantitative analysis of probabilistic pushdown automata: expectations and variances

Probabilistic pushdown automata (pPDA) have been identified as a natural model for probabilistic programs with recursive procedure calls. Previous works considered the decidability and complexity of the model-checking problem for pPDA and various probabilistic temporal logics. In this paper we concentrate on computing the expected values and variances of various random variables defined over runs of a given probabilistic pushdown automaton. In particular, we show how to compute the expected accumulated reward and the expected gain for certain classes of reward functions. Using these results, we show how to analyze various quantitative properties of pPDA that are not expressible in conventional probabilistic temporal logics.

Decisive Markov Chains

We consider qualitative and quantitative verification problems for infinite- state Markov chains. We call a Markov chain decisive w.r.t. a given set of target states F if it almost certainly eventually reaches either F or a state from which F can no longer be reached. While all finite Markov chains are trivially decisive (for every set F), this also holds for many classes of infinite Markov chains. Infinite Markov chains which contain a finite attractor are decisive w.r.t. every set F. In particular, all Markov chains induced by probabilistic lossy channel systems (PLCS) con- tain a finite attractor and are thus decisive. Furthermore, all globally coarse Markov chains are decisive. The class of globally coarse Markov chains includes, e.g., those induced by probabilistic vector addition systems (PVASS) with upward-closed sets F, and all Markov chains induced by probabilistic noisy Turing machines (PNTM) (a generalization of the noisy Turing machines (NTM) of Asarin and Collins). We consider both safety and liveness problems for decisive Markov chains. Safety: What is the probability that a given set of states F is eventually reached. Liveness: What is the probability that a given set of states is reached infinitely often. There are three variants of these questions. (1) The qualitative problem, i.e., deciding if the probability is one (or zero); (2) the approximate quantitative problem, i.e., computing the probability up-to arbitrary precision; (3) the exact quantitative problem, i.e., computing probabilities exactly. 1. We express the qualitative problem in abstract terms for decisive Markov chains, and show an almost complete picture of its decidability for PLCS, PVASS and PNTM. 2. We also show that the path enumeration algorithm of Iyer and Narasimha terminates for decisive Markov chains and can thus be used to solve the approximate quantitative safety problem. A modified variant of this algorithm can be used to solve the approximate quantitative liveness problem. 3. Finally, we show that the exact probability of (repeatedly) reaching F cannot be effectively expressed (in a uniform way) in Tarski-algebra for either PLCS, PVASS or (P)NTM (unlike for probabilistic pushdown automata).