Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control最新文献

Automatic Abstraction Refinement in Neural Network Verification using Sensitivity Analysis 基于灵敏度分析的神经网络验证的自动抽象细化

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-05-09 DOI: 10.1145/3575870.3587129

Tobias Ladner, M. Althoff

引用次数: 2

Poster Abstract: Sampling-based Approach to Robust STL Synthesis for Complex Systems under Uncertainty 摘要:基于采样的复杂系统鲁棒STL综合方法

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-05-09 DOI: 10.1145/3575870.3589551

Qi Heng Ho, Roland Ilyes, Zachary Sunberg, Morteza Lahijanian

引用次数: 0

Poster Abstract: Towards Seamless Reactivity of Hybrid Control 摘要:迈向混合控制的无缝反应性

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-05-09 DOI: 10.1145/3575870.3589553

Lucas N. Egidio, S. Nayak, M. D. Rossa, Anne-Kathrin Schmuck, R. Jungers

引用次数: 0

Reachability Analysis for Linear Systems with Uncertain Parameters using Polynomial Zonotopes 参数不确定线性系统的可达性分析

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-05-09 DOI: 10.1145/3575870.3587130

Ertai Luo, Niklas Kochdumper, Stanley Bak

引用次数: 1

Poster: Convex Scenario Optimisation for ReLU Networks 海报:面向ReLU网络的凸场景优化

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-05-09 DOI: 10.1145/3575870.3589552

Andrea Peruffo, Manuel Mazo Jr.

引用次数: 0

Lazy Synthesis of Symbolic Output-Feedback Controllers for State-Based Safety Specifications 基于状态安全规范的符号输出反馈控制器的惰性综合

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-05-09 DOI: 10.1145/3575870.3587111

Mehrdad Zareian, Anne-Kathrin Schmuck

引用次数: 0

Demo Abstract: Analysing CPS Security with Falsification on the Microsoft Flight Simulator 摘要:在微软飞行模拟器上分析CPS的安全性

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-05-09 DOI: 10.1145/3575870.3589550

Tanmay Khandait, Aniruddh Chandratre, Walstan Baptista, Giulia Pedrielli, Georgios Fainekos

引用次数: 0

Fully-Automated Verification of Linear Systems Using Reachability Analysis with Support Functions 使用支持函数的可达性分析的线性系统的全自动验证

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-05-09 DOI: 10.1145/3575870.3587121

Mark Wetzlinger, Niklas Kochdumper, Stanley Bak, M. Althoff

{"title":"Fully-Automated Verification of Linear Systems Using Reachability Analysis with Support Functions","authors":"Mark Wetzlinger, Niklas Kochdumper, Stanley Bak, M. Althoff","doi":"10.1145/3575870.3587121","DOIUrl":"https://doi.org/10.1145/3575870.3587121","url":null,"abstract":"While reachability analysis is one of the major techniques for formal verification of dynamical systems, the requirement to adequately tune algorithm parameters often prevents its widespread use in practical applications. In this work, we fully automate the verification process for linear time-invariant systems: Based on the computation of tight upper and lower bounds for the support function of the reachable set along a given direction, we present a fully-automated verification algorithm, which is based on iterative refinement of the upper and lower bounds and thus always returns the correct result in decidable cases. While this verification algorithm is particularly well suited for cases where the specifications are represented by halfspace constraints, we extend it to arbitrary convex unsafe sets using the Gilbert-Johnson-Keerthi algorithm. In summary, our automated verifier is applicable to arbitrary convex initial sets, input sets, as well as unsafe sets, can handle time-varying inputs, automatically returns a counterexample in case of a safety violation, and scales to previously unanalyzable high-dimensional state spaces. Our evaluation on several challenging benchmarks shows significant improvements in computational efficiency compared to verification using other state-of-the-art reachability tools.","PeriodicalId":426801,"journal":{"name":"Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130163707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Poster Abstract: Permissiveness for Strategy Adaptation 海报摘要:策略适应的容忍度

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-05-09 DOI: 10.1145/3575870.3589555

Ashwani Anand, S. Nayak, Anne-Kathrin Schmuck

引用次数: 0

Quantitative Verification for Neural Networks using ProbStars 利用ProbStars对神经网络进行定量验证

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-05-09 DOI: 10.1145/3575870.3587112

Hoang-Dung Tran, Sungwoo Choi, Hideki Okamoto, Bardh Hoxha, Georgios Fainekos, D. Prokhorov

{"title":"Quantitative Verification for Neural Networks using ProbStars","authors":"Hoang-Dung Tran, Sungwoo Choi, Hideki Okamoto, Bardh Hoxha, Georgios Fainekos, D. Prokhorov","doi":"10.1145/3575870.3587112","DOIUrl":"https://doi.org/10.1145/3575870.3587112","url":null,"abstract":"Most deep neural network (DNN) verification research focuses on qualitative verification, which answers whether or not a DNN violates a safety/robustness property. This paper proposes an approach to convert qualitative verification into quantitative verification for neural networks. The resulting quantitative verification method not only can answer YES or NO questions but also can compute the probability of a property being violated. To do that, we introduce the concept of a probabilistic star (or shortly ProbStar), a new variant of the well-known star set, in which the predicate variables belong to a Gaussian distribution and propose an approach to compute the probability of a probabilistic star in high-dimensional space. Unlike existing works dealing with constrained input sets, our work considers the input set as a truncated multivariate normal (Gaussian) distribution, i.e., besides the constraints on the input variables, the input set has a probability of the constraints being satisfied. The input distribution is represented as a probabilistic star set and is propagated through a network to construct the output reachable set containing multiple ProbStars, which are used to verify the safety or robustness properties of the network. In case of a property is violated, the violation probability can be computed precisely by an exact verification algorithm or approximately by an overapproximate verification algorithm. The proposed approach is implemented in a tool named StarV and is evaluated using the well-known ACASXu networks and a rocket landing benchmark.","PeriodicalId":426801,"journal":{"name":"Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116858384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1