Heuristics for the refinement of assumptions in generalized reactivity formulae

Reactive synthesis is concerned with automatically generating implementations from formal specifications. These specifications are typically written in the language of generalized reactivity (GR(1)), a subset of linear temporal logic capable of expressing the most common industrial specification patterns, and describe the requirements about the behavior of a system under assumptions about the environment where the system is to be deployed. Oftentimes no implementation exists which guarantees the required behavior under all possible environments, typically due to missing assumptions (this is usually referred to as unrealizability). To address this issue, new assumptions need to be added to complete the specification, a problem known as assumptions refinement. Since the space of candidate assumptions is intractably large, searching for the best solutions is inherently hard. In particular, new methods are needed to (i) increase the effectiveness of the search procedures, measured as the ratio between the number of solutions found and of refinements explored; and (ii) improve the results' quality, defined as the weakness of the solutions. In this thesis we propose a set of heuristics to meet these goals, and a methodology to assess and compare assumptions refinement methods based on quantitative metrics. The heuristics are in the form of algorithms to generate candidate refinements during the search, and quantitative measures to assess the quality of the candidates. We first discuss a heuristic method to generate assumptions that target the cause of unrealizability. This is done by selecting candidate refinement formulas based on Craig's interpolation. We provide a formal underpinning of the technique and evaluate it in terms of our new metric of effectiveness, as defined above, whose value is improved with respect to the state of the art. We demonstrate this on a set of popular benchmarks of embedded software. We then provide a formal, quantitative characterization of the permissiveness of environment assumptions in the form of a weakness measure. We prove that the partial order induced by this measure is consistent with the one induced by implication. The key advantage of this measure is that it allows for prioritizing candidate solutions, as we show experimentally. Lastly, we propose a notion of minimal refinements with respect to the observed counterstrategies. We demonstrate that exploring minimal refinements produces weaker solutions, and reduces the amount of computations needed to explore each refinement. However, this may come at the cost of reducing the effectiveness of the search. To counteract this effect, we propose a hybrid search approach in which both minimal and non-minimal refinements are explored.Open Acces

Alternative Automata-based Approaches to Probabilistic Model Checking

In this thesis we focus on new methods for probabilistic model checking (PMC) with linear temporal logic (LTL). The standard approach translates an LTL formula into a deterministic ω-automaton with a double-exponential blow up. There are approaches for Markov chain analysis against LTL with exponential runtime, which motivates the search for non-deterministic automata with restricted forms of non-determinism that make them suitable for PMC. For MDPs, the approach via deterministic automata matches the double-exponential lower bound, but a practical application might benefit from approaches via non-deterministic automata. We first investigate good-for-games (GFG) automata. In GFG automata one can resolve the non-determinism for a finite prefix without knowing the infinite suffix and still obtain an accepting run for an accepted word. We explain that GFG automata are well-suited for MDP analysis on a theoretic level, but our experiments show that GFG automata cannot compete with deterministic automata. We have also researched another form of pseudo-determinism, namely unambiguity, where for every accepted word there is exactly one accepting run. We present a polynomial-time approach for PMC of Markov chains against specifications given by an unambiguous Büchi automaton (UBA). Its two key elements are the identification whether the induced probability is positive, and if so, the identification of a state set inducing probability 1. Additionally, we examine the new symbolic Muller acceptance described in the Hanoi Omega Automata Format, which we call Emerson-Lei acceptance. It is a positive Boolean formula over unconditional fairness constraints. We present a construction of small deterministic automata using Emerson-Lei acceptance. Deciding, whether an MDP has a positive maximal probability to satisfy an Emerson-Lei acceptance, is NP-complete. This fact has triggered a DPLL-based algorithm for deciding positiveness

Proceedings of SUMo and CompoNet 2011

International audienc

Index appearance record with preorders

Transforming ω-automata into parity automata is traditionally done using appearance records. We present an efficient variant of this idea, tailored to Rabin automata, and several optimizations applicable to all appearance records. We compare the methods experimentally and show that our method produces significantly smaller automata than previous approaches

Fair Testing

In this paper we present a solution to the long-standing problem of characterising the coarsest liveness-preserving pre-congruence with respect to a full (TCSP-inspired) process algebra. In fact, we present two distinct characterisations, which give rise to the same relation: an operational one based on a De Nicola-Hennessy-like testing modality which we call should-testing, and a denotational one based on a refined notion of failures. One of the distinguishing characteristics of the should-testing pre-congruence is that it abstracts from divergences in the same way as Milner¿s observation congruence, and as a consequence is strictly coarser than observation congruence. In other words, should-testing has a built-in fairness assumption. This is in itself a property long sought-after; it is in notable contrast to the well-known must-testing of De Nicola and Hennessy (denotationally characterised by a combination of failures and divergences), which treats divergence as catrastrophic and hence is incompatible with observation congruence. Due to these characteristics, should-testing supports modular reasoning and allows to use the proof techniques of observation congruence, but also supports additional laws and techniques. Moreover, we show decidability of should-testing (on the basis of the denotational characterisation). Finally, we demonstrate its advantages by the application to a number of examples, including a scheduling problem, a version of the Alternating Bit-protocol, and fair lossy communication channel

Finite-State Abstractions for Probabilistic Computation Tree Logic

Probabilistic Computation Tree Logic (PCTL) is the established temporal logic for probabilistic verification of discrete-time Markov chains. Probabilistic model checking is a technique that verifies or refutes whether a property specified in this logic holds in a Markov chain. But Markov chains are often infinite or too large for this technique to apply. A standard solution to this problem is to convert the Markov chain to an abstract model and to model check that abstract model. The problem this thesis therefore studies is whether or when such finite abstractions of Markov chains for model checking PCTL exist. This thesis makes the following contributions. We identify a sizeable fragment of PCTL for which 3-valued Markov chains can serve as finite abstractions; this fragment is maximal for those abstractions and subsumes many practically relevant specifications including, e.g., reachability. We also develop game-theoretic foundations for the semantics of PCTL over Markov chains by capturing the standard PCTL semantics via a two-player games. These games, finally, inspire a notion of p-automata, which accept entire Markov chains. We show that p-automata subsume PCTL and Markov chains; that their languages of Markov chains have pleasant closure properties; and that the complexity of deciding acceptance matches that of probabilistic model checking for p-automata representing PCTL formulae. In addition, we offer a simulation between p-automata that under-approximates language containment. These results then allow us to show that p-automata comprise a solution to the problem studied in this thesis

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book