Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control最新文献_第3页

Poster Abstract: A Toolchain for Accelerated Symbolic Control 摘要:加速符号控制的工具链

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-05-09 DOI: 10.1145/3575870.3589554

R. Majumdar, Kaushik Mallik, Mateusz Rychlicki, Anne-Kathrin Schmuck, S. Soudjani

引用次数: 0

Continuity of Thresholded Mode-Switched ODEs and Digital Circuit Delay Models 阈值模式开关ode的连续性和数字电路延迟模型

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-03-24 DOI: 10.1145/3575870.3587125

Arman Ferdowsi, Matthias Fugger, Thomas Nowak, U. Schmid

引用次数: 2

SySCoRe: Synthesis via Stochastic Coupling Relations SySCoRe:基于随机耦合关系的合成

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2023-02-23 DOI: 10.1145/3575870.3587123

B. V. Huijgevoort, O. Schon, S. Soudjani, S. Haesaert

{"title":"SySCoRe: Synthesis via Stochastic Coupling Relations","authors":"B. V. Huijgevoort, O. Schon, S. Soudjani, S. Haesaert","doi":"10.1145/3575870.3587123","DOIUrl":"https://doi.org/10.1145/3575870.3587123","url":null,"abstract":"We present SySCoRe, a MATLAB toolbox that synthesizes controllers for stochastic continuous-state systems to satisfy temporal logic specifications. Starting from a system description and a co-safe temporal logic specification, SySCoRe provides all necessary functions for synthesizing a robust controller and quantifying the associated formal robustness guarantees. It distinguishes itself from other available tools by supporting nonlinear dynamics, complex co-safe temporal logic specifications over infinite horizons and model-order reduction. To achieve this, SySCoRe generates a finite-state abstraction of the provided model and performs probabilistic model checking. Then, it establishes a probabilistic coupling to the original stochastic system encoded in an approximate simulation relation, based on which a lower bound on the satisfaction probability is computed. SySCoRe provides non-trivial lower bounds for infinite-horizon properties and unbounded disturbances since its computed error does not grow linearly in the horizon of the specification. It exploits a tensor representation to facilitate the efficient computation of transition probabilities. We showcase these features on several benchmarks and compare the performance of the tool with existing tools.","PeriodicalId":426801,"journal":{"name":"Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129487992","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: 6

Distributionally Robust Strategy Synthesis for Switched Stochastic Systems 切换随机系统的分布鲁棒策略综合

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2022-12-29 DOI: 10.1145/3575870.3587127

Ibon Gracia, Dimitris Boskos, L. Laurenti, M. Mazo

{"title":"Distributionally Robust Strategy Synthesis for Switched Stochastic Systems","authors":"Ibon Gracia, Dimitris Boskos, L. Laurenti, M. Mazo","doi":"10.1145/3575870.3587127","DOIUrl":"https://doi.org/10.1145/3575870.3587127","url":null,"abstract":"We present a novel framework for formal control of uncertain discrete-time switched stochastic systems against probabilistic reach-avoid specifications. In particular, we consider stochastic systems with additive noise, whose distribution lies in an ambiguity set of distributions that are ε − close to a nominal one according to the Wasserstein distance. For this class of systems we derive control synthesis algorithms that are robust against all these distributions and maximize the probability of satisfying a reach-avoid specification, defined as the probability of reaching a goal region while being safe. The framework we present first learns an abstraction of a switched stochastic system as a robust Markov decision process (robust MDP) by accounting for both the stochasticity of the system and the uncertainty in the noise distribution. Then, it synthesizes a strategy on the resulting robust MDP that maximizes the probability of satisfying the property and is robust to all uncertainty in the system. This strategy is then refined into a switching strategy for the original stochastic system. By exploiting tools from optimal transport and stochastic programming, we show that synthesizing such a strategy reduces to solving a set of linear programs, thus guaranteeing efficiency. We experimentally validate the efficacy of our framework on various case studies, including both linear and non-linear switched stochastic systems. Our results represent the first formal approach for control synthesis of stochastic systems with uncertain noise distribution.","PeriodicalId":426801,"journal":{"name":"Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126122903","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: 4

BERN-NN: Tight Bound Propagation For Neural Networks Using Bernstein Polynomial Interval Arithmetic 基于Bernstein多项式区间算法的神经网络紧界传播

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2022-11-22 DOI: 10.1145/3575870.3587126

Wael Fatnassi, Haitham Khedr, Valen Yamamoto, Yasser Shoukry

{"title":"BERN-NN: Tight Bound Propagation For Neural Networks Using Bernstein Polynomial Interval Arithmetic","authors":"Wael Fatnassi, Haitham Khedr, Valen Yamamoto, Yasser Shoukry","doi":"10.1145/3575870.3587126","DOIUrl":"https://doi.org/10.1145/3575870.3587126","url":null,"abstract":"In this paper, we present BERN-NN as an efficient tool to perform bound propagation of Neural Networks (NNs). Bound propagation is a critical step in wide range of NN model checkers and reachability analysis tools. Given a bounded input set, bound propagation algorithms aim to compute tight bounds on the output of the NN. So far, linear and convex optimizations have been used to perform bound propagation. Since neural networks are highly non-convex, state-of-the-art bound propagation techniques suffer from introducing large errors. To circumvent such drawback, BERN-NN approximates the bounds of each neuron using a class of polynomials called Bernstein polynomials. Bernstein polynomials enjoy several interesting properties that allow BERN-NN to obtain tighter bounds compared to those relying on linear and convex approximations. BERN-NN is efficiently parallelized on graphic processing units (GPUs). Extensive numerical results show that bounds obtained by BERN-NN are orders of magnitude tighter than those obtained by state-of-the-art verifiers such as linear programming and linear interval arithmetic. Moreoveer, BERN-NN is both faster and produces tighter outputs compared to convex programming approaches like alpha-CROWN.","PeriodicalId":426801,"journal":{"name":"Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127580958","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: 2

An STL-based Approach to Resilient Control for Cyber-Physical Systems 基于stl的网络物理系统弹性控制方法

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2022-11-05 DOI: 10.1145/3575870.3587119

Hongkai Chen, S. Smolka, Nicola Paoletti, Shanny Lin

{"title":"An STL-based Approach to Resilient Control for Cyber-Physical Systems","authors":"Hongkai Chen, S. Smolka, Nicola Paoletti, Shanny Lin","doi":"10.1145/3575870.3587119","DOIUrl":"https://doi.org/10.1145/3575870.3587119","url":null,"abstract":"We present ResilienC, a framework for resilient control of Cyber-Physical Systems subject to STL-based requirements. ResilienC utilizes a recently developed formalism for specifying CPS resiliency in terms of sets of (rec, dur) real-valued pairs, where rec represents the system’s capability to rapidly recover from a property violation (recoverability), and dur is reflective of its ability to avoid violations post-recovery (durability). We define the resilient STL control problem as one of multi-objective optimization, where the recoverability and durability of the desired STL specification are maximized. When neither objective is prioritized over the other, the solution to the problem is a set of Pareto-optimal system trajectories. We present a precise solution method to the resilient STL control problem using a mixed-integer linear programming encoding and an a posteriori ϵ -constraint approach for efficiently retrieving the complete set of optimally resilient solutions. In ResilienC, at each time-step, the optimal control action selected from the set of Pareto-optimal solutions by a Decision Maker strategy realizes a form of Model Predictive Control. We demonstrate the practical utility of the ResilienC framework on two significant case studies: autonomous vehicle lane keeping and deadline-driven, multi-region package delivery.","PeriodicalId":426801,"journal":{"name":"Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129175898","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

Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes 随机过程STL需求的保形定量预测监测

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2022-11-04 DOI: 10.1145/3575870.3587113

Francesca Cairoli, Nicola Paoletti, L. Bortolussi

{"title":"Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes","authors":"Francesca Cairoli, Nicola Paoletti, L. Bortolussi","doi":"10.1145/3575870.3587113","DOIUrl":"https://doi.org/10.1145/3575870.3587113","url":null,"abstract":"We consider the problem of predictive monitoring (PM), i.e., predicting at runtime the satisfaction of a desired property from the current system’s state. Due to its relevance for runtime safety assurance and online control, PM methods need to be efficient to enable timely interventions against predicted violations, while providing correctness guarantees. We introduce quantitative predictive monitoring (QPM), the first PM method to support stochastic processes and rich specifications given in Signal Temporal Logic (STL). Unlike most of the existing PM techniques that predict whether or not some property ϕ is satisfied, QPM provides a quantitative measure of satisfaction by predicting the quantitative (aka robust) STL semantics of ϕ. QPM derives prediction intervals that are highly efficient to compute and with probabilistic guarantees, in that the intervals cover with arbitrary probability the STL robustness values relative to the stochastic evolution of the system. To do so, we take a machine-learning approach and leverage recent advances in conformal inference for quantile regression, thereby avoiding expensive Monte Carlo simulations at runtime to estimate the intervals. We also show how our monitors can be combined in a compositional manner to handle composite formulas, without retraining the predictors or sacrificing the guarantees. We demonstrate the effectiveness and scalability of QPM over a benchmark of four discrete-time stochastic processes with varying degrees of complexity.","PeriodicalId":426801,"journal":{"name":"Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125565520","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: 5

Interval Markov Decision Processes with Continuous Action-Spaces 具有连续动作空间的区间马尔可夫决策过程

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2022-11-02 DOI: 10.1145/3575870.3587117

Giannis Delimpaltadakis, Morteza Lahijanian, M. Mazo, L. Laurenti

{"title":"Interval Markov Decision Processes with Continuous Action-Spaces","authors":"Giannis Delimpaltadakis, Morteza Lahijanian, M. Mazo, L. Laurenti","doi":"10.1145/3575870.3587117","DOIUrl":"https://doi.org/10.1145/3575870.3587117","url":null,"abstract":"Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on employing IMDPs as abstractions of stochastic systems for control synthesis. However, due to the absence of algorithms for synthesis over IMDPs with continuous action-spaces, the action-space is assumed discrete a-priori, which is a restrictive assumption for many applications. Motivated by this, we introduce continuous-action IMDPs (caIMDPs), where the bounds on transition probabilities are functions of the action variables, and study value iteration for maximizing expected cumulative rewards. Specifically, we decompose the max-min problem associated to value iteration to |𝒬| max problems, where |𝒬| is the number of states of the caIMDP. Then, exploiting the simple form of these max problems, we identify cases where value iteration over caIMDPs can be solved efficiently (e.g., with linear or convex programming). We also gain other interesting insights: e.g., in certain cases where the action set 𝒜 is a polytope, synthesis over a discrete-action IMDP, where the actions are the vertices of 𝒜, is sufficient for optimality. We demonstrate our results on a numerical example. Finally, we include a short discussion on employing caIMDPs as abstractions for control synthesis.","PeriodicalId":426801,"journal":{"name":"Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122927799","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: 3

Poster Abstract: Stability Analysis of Planar Probabilistic Piecewise Constant Derivative Systems 摘要:平面概率分段常数导数系统的稳定性分析

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control Pub Date : 2021-09-17 DOI: 10.1007/978-3-031-16336-4_10

Spandan Das, P. Prabhakar

引用次数: 1