174 research outputs found

    Conformance Testing for Stochastic Cyber-Physical Systems

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    Conformance is defined as a measure of distance between the behaviors of two dynamical systems. The notion of conformance can accelerate system design when models of varying fidelities are available on which analysis and control design can be done more efficiently. Ultimately, conformance can capture distance between design models and their real implementations and thus aid in robust system design. In this paper, we are interested in the conformance of stochastic dynamical systems. We argue that probabilistic reasoning over the distribution of distances between model trajectories is a good measure for stochastic conformance. Additionally, we propose the non-conformance risk to reason about the risk of stochastic systems not being conformant. We show that both notions have the desirable transference property, meaning that conformant systems satisfy similar system specifications, i.e., if the first model satisfies a desirable specification, the second model will satisfy (nearly) the same specification. Lastly, we propose how stochastic conformance and the non-conformance risk can be estimated from data using statistical tools such as conformal prediction. We present empirical evaluations of our method on an F-16 aircraft, an autonomous vehicle, a spacecraft, and Dubin's vehicle

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization

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    Despite the success of neural-based combinatorial optimization methods for end-to-end heuristic learning, out-of-distribution generalization remains a challenge. In this paper, we present a novel formulation of Combinatorial Optimization Problems (COPs) as Markov Decision Processes (MDPs) that effectively leverages common symmetries of COPs to improve out-of-distribution robustness. Starting from a direct MDP formulation of a constructive method, we introduce a generic way to reduce the state space, based on Bisimulation Quotienting (BQ) in MDPs. Then, for COPs with a recursive nature, we specialize the bisimulation and show how the reduced state exploits the symmetries of these problems and facilitates MDP solving. Our approach is principled and we prove that an optimal policy for the proposed BQ-MDP actually solves the associated COPs. We illustrate our approach on five classical problems: the Euclidean and Asymmetric Traveling Salesman, Capacitated Vehicle Routing, Orienteering and Knapsack Problems. Furthermore, for each problem, we introduce a simple attention-based policy network for the BQ-MDPs, which we train by imitation of (near) optimal solutions of small instances from a single distribution. We obtain new state-of-the-art results for the five COPs on both synthetic and realistic benchmarks. Notably, in contrast to most existing neural approaches, our learned policies show excellent generalization performance to much larger instances than seen during training, without any additional search procedure

    Formal synthesis of partially-observable cyber-physical systems

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    This dissertation is motivated by the challenges arising in the synthesis of controllers for partially-observable cyber-physical systems (PO-CPSs). In the past decade, CPSs have become ubiquitous and an integral part of our daily lives. Examples of such systems range from autonomous vehicles, drones, and aircraft to robots and advanced manufacturing. In many applications, these systems are expected to do complex logic tasks. Such tasks can usually be expressed using temporal logic formulae or as (in)finite strings over finite automata. In the past few years, abstraction-based techniques have been very promising for the formal synthesis of controllers. Since these techniques are based on the discretization of state and input sets, when dealing with large-scale systems, unfortunately, they suffer severely from the curse of dimensionality (i.e., the computational complexity grows exponentially with the dimension of the state set). In order to overcome the large computa- tional burden, a discretization-free approach based on control barrier functions has shown great potential to solve formal synthesis problems. In this thesis, we provide a systematic approach to synthesize a hybrid control policy for partially-observable (stochastic) control systems without discretizing the state sets. In many real-life applications, full-state information is not always available (due to the cost of sensing or the unavailability of the measurements). Therefore, in this thesis, we consider partially-observable (stochastic) control systems. Given proper state estimators, our goal is to utilize a notion of control barrier functions to synthesize control policies that provide (and potentially maximize) a lower bound on the probability that the trajectories of the partially-observable (stochastic) control system satisfy complex logic specifications such as safety and those that can be expressed as deterministic finite automata (DFA). Two main approaches are presented in this thesis to construct control barrier functions. In the first approach, no prior knowledge of estimation accuracy is needed. The second approach utilizes a (probability) bound on the estimation accuracy. Though the synthesis procedure for lower-dimensional systems is challenging itself, the task is much more computationally expensive (if not impossible) for large-scale interconnected systems. To overcome the challenges encountered with large-scale systems, we develop approaches to reduce the computational complexity. In particular, by considering a large-scale partially-observable control system as an interconnection of lower-dimensional subsystems, we compute so-called local control barrier functions for subsystems along with the corresponding local controllers. By assuming some small-gain type conditions, we then utilize local control barrier functions of subsystems to compositionally construct an overall control barrier function for the interconnected system. Finally, since closed-form mathematical models of many physical systems are either unavailable or too complicated to be of any use, we also extend our work to the synthesis of safety controllers for partially-observable systems with unknown dynamics. To tackle this problem, we utilize a data-driven approach and construct control barrier functions and their corresponding controllers via sets of data collected from the output trajectories of the systems and the trajectories of the estimators. To demonstrate the effectiveness of the proposed results in the thesis, we consider various case studies, such as a DC motor, an adaptive cruise control (ACC) system consisting of vehicles in a platoon, and a Moore-Greitzer jet engine model

    Meniscus dynamics and evaporation in inkjet printing

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    Foundations of Software Science and Computation Structures

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    This open access book constitutes the proceedings of the 25th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2022, which was held during April 4-6, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 23 regular papers presented in this volume were carefully reviewed and selected from 77 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems

    Tools and Algorithms for the Construction and Analysis of Systems

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    This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

    Formal Methods and Safety for Automated Vehicles: Modeling, Abstractions, and Synthesis of Tactical Planners

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    One goal of developing automated road vehicles is to completely free people from driving tasks. Automated vehicles with no human driver must handle all traffic situations that human drivers are expected to handle, possibly more. Though human drivers cause a lot of traffic accidents, they still have a very low accident and failure rate that automated vehicles must match.Tactical planners are responsible for making discrete decisions for the coming seconds or minutes. As with all subsystems in an automated vehicle, these planners need to be supported with a credible and convincing argument of their correctness. The planners interact with other road users in a feedback loop, so their correctness depends on their behavior in relation to other drivers and road users over time. One way to ascertain their correctness is to test the vehicles in real traffic. But to be sufficiently certain that a tactical planner is safe, it has to be tested on 255 million miles with no accidents.Formal methods can, in contrast to testing, mathematically prove that given requirements are fulfilled. Hence, these methods are a promising alternative for making credible arguments for tactical planners’ correctness. The topic of this thesis is the use of formal methods in the automotive industry to design safe tactical planners. What is interesting is both how automotive systems can be modeled in formal frameworks, and how formal methods can be used practically within the automotive development process.The main findings of this thesis are that it is viable to formally express desired properties of tactical planners, and to use formal methods to prove their correctness. However, the difficulty to anticipate and inspect the interaction of several desired properties is found to be an obstacle. Model Checking, Reactive Synthesis, and Supervisory Control Theory have been used in the design and development process of tactical planners, and these methods have their benefits, depending on the application. To be feasible and useful, these methods need to operate on both a high and a low level of abstraction, and this thesis contributes an automatic abstraction method that bridges this divide.It is also found that artifacts from formal methods tools may be used to convincingly argue that a realization of a tactical planner is safe, and that such an argument puts formal requirements on the vehicle’s other subsystems and its surroundings

    Programming Languages and Systems

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    This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

    Computer Aided Verification

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    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
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