TY - CONF AB - Binary decision diagrams (BDDs) are one of the fundamental data structures in formal methods and computer science in general. However, the performance of BDD-based algorithms greatly depends on memory latency due to the reliance on large hash tables and thus, by extension, on the speed of random memory access. This hinders the full utilisation of resources available on modern CPUs, since the absolute memory latency has not improved significantly for at least a decade. In this paper, we explore several implementation techniques that improve the performance of BDD manipulation either through enhanced memory locality or by partially eliminating random memory access. On a benchmark suite of 600+ BDDs derived from real-world applications, we demonstrate runtime that is comparable or better than parallelising the same operations on eight CPU cores. AU - Pastva, Samuel AU - Henzinger, Thomas A ID - 14718 SN - 9783854480600 T2 - Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design TI - Binary decision diagrams on modern hardware ER - TY - CONF AB - We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for providing formal reach-avoid guarantees, which combine and generalize stability and safety guarantees, with a tolerable probability threshold p in [0,1] over the infinite time horizon. Our method leverages advances in machine learning literature and it represents formal certificates as neural networks. In particular, we learn a certificate in the form of a reach-avoid supermartingale (RASM), a novel notion that we introduce in this work. Our RASMs provide reachability and avoidance guarantees by imposing constraints on what can be viewed as a stochastic extension of level sets of Lyapunov functions for deterministic systems. Our approach solves several important problems -- it can be used to learn a control policy from scratch, to verify a reach-avoid specification for a fixed control policy, or to fine-tune a pre-trained policy if it does not satisfy the reach-avoid specification. We validate our approach on 3 stochastic non-linear reinforcement learning tasks. AU - Zikelic, Dorde AU - Lechner, Mathias AU - Henzinger, Thomas A AU - Chatterjee, Krishnendu ID - 14830 IS - 10 KW - General Medicine SN - 2159-5399 T2 - Proceedings of the 37th AAAI Conference on Artificial Intelligence TI - Learning control policies for stochastic systems with reach-avoid guarantees VL - 37 ER - TY - JOUR AB - Neural-network classifiers achieve high accuracy when predicting the class of an input that they were trained to identify. Maintaining this accuracy in dynamic environments, where inputs frequently fall outside the fixed set of initially known classes, remains a challenge. We consider the problem of monitoring the classification decisions of neural networks in the presence of novel classes. For this purpose, we generalize our recently proposed abstraction-based monitor from binary output to real-valued quantitative output. This quantitative output enables new applications, two of which we investigate in the paper. As our first application, we introduce an algorithmic framework for active monitoring of a neural network, which allows us to learn new classes dynamically and yet maintain high monitoring performance. As our second application, we present an offline procedure to retrain the neural network to improve the monitor’s detection performance without deteriorating the network’s classification accuracy. Our experimental evaluation demonstrates both the benefits of our active monitoring framework in dynamic scenarios and the effectiveness of the retraining procedure. AU - Kueffner, Konstantin AU - Lukina, Anna AU - Schilling, Christian AU - Henzinger, Thomas A ID - 13234 JF - International Journal on Software Tools for Technology Transfer SN - 1433-2779 TI - Into the unknown: Active monitoring of neural networks (extended version) VL - 25 ER - TY - JOUR AB - We consider fixpoint algorithms for two-player games on graphs with $\omega$-regular winning conditions, where the environment is constrained by a strong transition fairness assumption. Strong transition fairness is a widely occurring special case of strong fairness, which requires that any execution is strongly fair with respect to a specified set of live edges: whenever the source vertex of a live edge is visited infinitely often along a play, the edge itself is traversed infinitely often along the play as well. We show that, surprisingly, strong transition fairness retains the algorithmic characteristics of the fixpoint algorithms for $\omega$-regular games -- the new algorithms have the same alternation depth as the classical algorithms but invoke a new type of predecessor operator. For Rabin games with $k$ pairs, the complexity of the new algorithm is $O(n^{k+2}k!)$ symbolic steps, which is independent of the number of live edges in the strong transition fairness assumption. Further, we show that GR(1) specifications with strong transition fairness assumptions can be solved with a 3-nested fixpoint algorithm, same as the usual algorithm. In contrast, strong fairness necessarily requires increasing the alternation depth depending on the number of fairness assumptions. We get symbolic algorithms for (generalized) Rabin, parity and GR(1) objectives under strong transition fairness assumptions as well as a direct symbolic algorithm for qualitative winning in stochastic $\omega$-regular games that runs in $O(n^{k+2}k!)$ symbolic steps, improving the state of the art. Finally, we have implemented a BDD-based synthesis engine based on our algorithm. We show on a set of synthetic and real benchmarks that our algorithm is scalable, parallelizable, and outperforms previous algorithms by orders of magnitude. AU - Banerjee, Tamajit AU - Majumdar, Rupak AU - Mallik, Kaushik AU - Schmuck, Anne-Kathrin AU - Soudjani, Sadegh ID - 14920 JF - TheoretiCS SN - 2751-4838 TI - Fast symbolic algorithms for mega-regular games under strong transition fairness VL - 2 ER - TY - CONF AB - Partially specified Boolean networks (PSBNs) represent a promising framework for the qualitative modelling of biological systems in which the logic of interactions is not completely known. Phenotype control aims to stabilise the network in states exhibiting specific traits. In this paper, we define the phenotype control problem in the context of asynchronous PSBNs and propose a novel semi-symbolic algorithm for solving this problem with permanent variable perturbations. AU - Beneš, Nikola AU - Brim, Luboš AU - Pastva, Samuel AU - Šafránek, David AU - Šmijáková, Eva ID - 14411 SN - 0302-9743 T2 - 21st International Conference on Computational Methods in Systems Biology TI - Phenotype control of partially specified boolean networks VL - 14137 ER - TY - CONF AB - We present a flexible and efficient toolchain to symbolically solve (standard) Rabin games, fair-adversarial Rabin games, and 2 1/2 license type-player Rabin games. To our best knowledge, our tools are the first ones to be able to solve these problems. Furthermore, using these flexible game solvers as a back-end, we implemented a tool for computing correct-by-construction controllers for stochastic dynamical systems under LTL specifications. Our implementations use the recent theoretical result that all of these games can be solved using the same symbolic fixpoint algorithm but utilizing different, domain specific calculations of the involved predecessor operators. The main feature of our toolchain is the utilization of two programming abstractions: one to separate the symbolic fixpoint computations from the predecessor calculations, and another one to allow the integration of different BDD libraries as back-ends. In particular, we employ a multi-threaded execution of the fixpoint algorithm by using the multi-threaded BDD library Sylvan, which leads to enormous computational savings. AU - Majumdar, Rupak AU - Mallik, Kaushik AU - Rychlicki, Mateusz AU - Schmuck, Anne-Kathrin AU - Soudjani, Sadegh ID - 14758 SN - 0302-9743 T2 - 35th International Conference on Computer Aided Verification TI - A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties VL - 13966 ER - TY - GEN AB - This resource contains the artifacts for reproducing the experimental results presented in the paper titled "A Flexible Toolchain for Symbolic Rabin Games under Fair and Stochastic Uncertainties" that has been submitted in CAV 2023. AU - Majumdar, Rupak AU - Mallik, Kaushik AU - Rychlicki, Mateusz AU - Schmuck, Anne-Kathrin AU - Soudjani, Sadegh ID - 14994 TI - A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties ER - TY - CONF AB - Reinforcement learning has shown promising results in learning neural network policies for complicated control tasks. However, the lack of formal guarantees about the behavior of such policies remains an impediment to their deployment. We propose a novel method for learning a composition of neural network policies in stochastic environments, along with a formal certificate which guarantees that a specification over the policy's behavior is satisfied with the desired probability. Unlike prior work on verifiable RL, our approach leverages the compositional nature of logical specifications provided in SpectRL, to learn over graphs of probabilistic reach-avoid specifications. The formal guarantees are provided by learning neural network policies together with reach-avoid supermartingales (RASM) for the graph’s sub-tasks and then composing them into a global policy. We also derive a tighter lower bound compared to previous work on the probability of reach-avoidance implied by a RASM, which is required to find a compositional policy with an acceptable probabilistic threshold for complex tasks with multiple edge policies. We implement a prototype of our approach and evaluate it on a Stochastic Nine Rooms environment. AU - Zikelic, Dorde AU - Lechner, Mathias AU - Verma, Abhinav AU - Chatterjee, Krishnendu AU - Henzinger, Thomas A ID - 15023 T2 - 37th Conference on Neural Information Processing Systems TI - Compositional policy learning in stochastic control systems with formal guarantees ER - TY - CONF AB - Hyperproperties are properties that relate multiple execution traces. Previous work on monitoring hyperproperties focused on synchronous hyperproperties, usually specified in HyperLTL. When monitoring synchronous hyperproperties, all traces are assumed to proceed at the same speed. We introduce (multi-trace) prefix transducers and show how to use them for monitoring synchronous as well as, for the first time, asynchronous hyperproperties. Prefix transducers map multiple input traces into one or more output traces by incrementally matching prefixes of the input traces against expressions similar to regular expressions. The prefixes of different traces which are consumed by a single matching step of the monitor may have different lengths. The deterministic and executable nature of prefix transducers makes them more suitable as an intermediate formalism for runtime verification than logical specifications, which tend to be highly non-deterministic, especially in the case of asynchronous hyperproperties. We report on a set of experiments about monitoring asynchronous version of observational determinism. AU - Chalupa, Marek AU - Henzinger, Thomas A ID - 14076 SN - 978-3-031-44266-7 T2 - 23nd International Conference on Runtime Verification TI - Monitoring hyperproperties with prefix transducers VL - 14245 ER - TY - GEN AB - This artifact aims to reproduce experiments from the paper Monitoring Hyperproperties With Prefix Transducers accepted at RV'23, and give further pointers to implementation of prefix transducers. It has two parts: a pre-compiled docker image and sources that one can use to compile (locally or in docker) the software and run the experiments. AU - Chalupa, Marek AU - Henzinger, Thomas A ID - 15035 TI - Monitoring hyperproperties with prefix transducers ER -