International Conference on Quantitative Evaluation of Systems最新文献

On the Trade-off Between Efficiency and Precision of Neural Abstraction 神经抽象的效率与精度权衡

International Conference on Quantitative Evaluation of Systems Pub Date : 2023-07-28 DOI: 10.48550/arXiv.2307.15546

Alec Edwards, Mirco Giacobbe, A. Abate

{"title":"On the Trade-off Between Efficiency and Precision of Neural Abstraction","authors":"Alec Edwards, Mirco Giacobbe, A. Abate","doi":"10.48550/arXiv.2307.15546","DOIUrl":"https://doi.org/10.48550/arXiv.2307.15546","url":null,"abstract":"Neural abstractions have been recently introduced as formal approximations of complex, nonlinear dynamical models. They comprise a neural ODE and a certified upper bound on the error between the abstract neural network and the concrete dynamical model. So far neural abstractions have exclusively been obtained as neural networks consisting entirely of $ReLU$ activation functions, resulting in neural ODE models that have piecewise affine dynamics, and which can be equivalently interpreted as linear hybrid automata. In this work, we observe that the utility of an abstraction depends on its use: some scenarios might require coarse abstractions that are easier to analyse, whereas others might require more complex, refined abstractions. We therefore consider neural abstractions of alternative shapes, namely either piecewise constant or nonlinear non-polynomial (specifically, obtained via sigmoidal activations). We employ formal inductive synthesis procedures to generate neural abstractions that result in dynamical models with these semantics. Empirically, we demonstrate the trade-off that these different neural abstraction templates have vis-a-vis their precision and synthesis time, as well as the time required for their safety verification (done via reachability computation). We improve existing synthesis techniques to enable abstraction of higher-dimensional models, and additionally discuss the abstraction of complex neural ODEs to improve the efficiency of reachability analysis for these models.","PeriodicalId":150495,"journal":{"name":"International Conference on Quantitative Evaluation of Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127917977","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: 0

Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version) 更安全强化学习的概率反例指南(扩展版)

International Conference on Quantitative Evaluation of Systems Pub Date : 2023-07-10 DOI: 10.48550/arXiv.2307.04927

Xiaotong Ji, Antonio Filieri

{"title":"Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)","authors":"Xiaotong Ji, Antonio Filieri","doi":"10.48550/arXiv.2307.04927","DOIUrl":"https://doi.org/10.48550/arXiv.2307.04927","url":null,"abstract":"Safe exploration aims at addressing the limitations of Reinforcement Learning (RL) in safety-critical scenarios, where failures during trial-and-error learning may incur high costs. Several methods exist to incorporate external knowledge or to use proximal sensor data to limit the exploration of unsafe states. However, reducing exploration risks in unknown environments, where an agent must discover safety threats during exploration, remains challenging. In this paper, we target the problem of safe exploration by guiding the training with counterexamples of the safety requirement. Our method abstracts both continuous and discrete state-space systems into compact abstract models representing the safety-relevant knowledge acquired by the agent during exploration. We then exploit probabilistic counterexample generation to construct minimal simulation submodels eliciting safety requirement violations, where the agent can efficiently train offline to refine its policy towards minimising the risk of safety violations during the subsequent online exploration. We demonstrate our method's effectiveness in reducing safety violations during online exploration in preliminary experiments by an average of 40.3% compared with QL and DQN standard algorithms and 29.1% compared with previous related work, while achieving comparable cumulative rewards with respect to unrestricted exploration and alternative approaches.","PeriodicalId":150495,"journal":{"name":"International Conference on Quantitative Evaluation of Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129503742","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: 0

Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics 不确定马尔可夫跳变线性系统的形式控制器综合

International Conference on Quantitative Evaluation of Systems Pub Date : 2022-12-01 DOI: 10.48550/arXiv.2212.00679

Luke Rickard, Thom S. Badings, Licio Romao, N. Jansen, A. Abate

{"title":"Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics","authors":"Luke Rickard, Thom S. Badings, Licio Romao, N. Jansen, A. Abate","doi":"10.48550/arXiv.2212.00679","DOIUrl":"https://doi.org/10.48550/arXiv.2212.00679","url":null,"abstract":"Automated synthesis of provably correct controllers for cyber-physical systems is crucial for deployment in safety-critical scenarios. However, hybrid features and stochastic or unknown behaviours make this problem challenging. We propose a method for synthesising controllers for Markov jump linear systems (MJLSs), a class of discrete-time models for cyber-physical systems, so that they certifiably satisfy probabilistic computation tree logic (PCTL) formulae. An MJLS consists of a finite set of stochastic linear dynamics and discrete jumps between these dynamics that are governed by a Markov decision process (MDP). We consider the cases where the transition probabilities of this MDP are either known up to an interval or completely unknown. Our approach is based on a finite-state abstraction that captures both the discrete (mode-jumping) and continuous (stochastic linear) behaviour of the MJLS. We formalise this abstraction as an interval MDP (iMDP) for which we compute intervals of transition probabilities using sampling techniques from the so-called 'scenario approach', resulting in a probabilistically sound approximation. We apply our method to multiple realistic benchmark problems, in particular, a temperature control and an aerial vehicle delivery problem.","PeriodicalId":150495,"journal":{"name":"International Conference on Quantitative Evaluation of Systems","volume":"14 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123680581","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

Rate Lifting for Stochastic Process Algebra: Exploiting Structural Properties 随机过程代数的速率提升:利用结构性质

International Conference on Quantitative Evaluation of Systems Pub Date : 2022-06-29 DOI: 10.48550/arXiv.2206.14505

M. Siegle, Amin Soltanieh

引用次数: 0

Distribution Estimation for Probabilistic Loops 概率环的分布估计

International Conference on Quantitative Evaluation of Systems Pub Date : 2022-05-13 DOI: 10.1007/978-3-031-16336-4_2

A. Karimi, Marcel Moosbrugger, Miroslav Stankovivc, Laura Kov'acs, E. Bartocci, E. Bura

引用次数: 2

Moment-based Invariants for Probabilistic Loops with Non-polynomial Assignments 非多项式赋值概率环的矩基不变量

International Conference on Quantitative Evaluation of Systems Pub Date : 2022-05-05 DOI: 10.48550/arXiv.2205.02577

Andrey Kofnov, Marcel Moosbrugger, Miroslav Stankovivc, E. Bartocci, E. Bura

{"title":"Moment-based Invariants for Probabilistic Loops with Non-polynomial Assignments","authors":"Andrey Kofnov, Marcel Moosbrugger, Miroslav Stankovivc, E. Bartocci, E. Bura","doi":"10.48550/arXiv.2205.02577","DOIUrl":"https://doi.org/10.48550/arXiv.2205.02577","url":null,"abstract":". We present a method to automatically approximate moment-based invariants of probabilistic programs with non-polynomial updates of continuous state variables to accommodate more complex dynamics. Our approach leverages polynomial chaos expansion to approximate nonlinear functional updates as sums of orthogonal polynomials. We exploit this result to automatically estimate state-variable moments of all orders in Prob-solvable loops with non-polynomial updates. We showcase the accuracy of our estimation approach in several examples, such as the turning vehicle model and the Taylor rule in monetary policy. expansion to approximate non-polynomial general functional assignments. The approximations produced by our technique have optimal exponential convergence when the parameters of the general non-polynomial functions have distributions that are stable across all iterations. We derived an upper bound on the approximation error for the case of un-stable parameter distributions. Our methods can accommodate non-linear, non-polynomial updates in classes of probabilistic loops amenable to automated moment computation, such as the class of Prob-solvable loops. Moreover, our tech-niques can be used for moment approximation for uncertainty quantiﬁcation in more general probabilistic loops. Our experiments demonstrate the ability of our methods to characterize non-polynomial behavior in stochastic models from various domains via their moments, with high accuracy and in a fraction of the time required by other state-of-the-art tools.","PeriodicalId":150495,"journal":{"name":"International Conference on Quantitative Evaluation of Systems","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130341652","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

Active and sparse methods in smoothed model checking 光滑模型检验中的主动和稀疏方法

International Conference on Quantitative Evaluation of Systems Pub Date : 2021-04-20 DOI: 10.1007/978-3-030-85172-9_12

Paul Piho, J. Hillston

引用次数: 2

SEH: Size Estimate Hedging for Single-Server Queues SEH:单服务器队列的大小估计对冲

International Conference on Quantitative Evaluation of Systems Pub Date : 2020-12-30 DOI: 10.1007/978-3-030-85172-9_9

Maryam Akbari-Moghaddam, D. Down

引用次数: 3

Loss-Size and Reliability Trade-Offs Amongst Diverse Redundant Binary Classifiers 不同冗余二值分类器之间的损失大小和可靠性权衡

International Conference on Quantitative Evaluation of Systems Pub Date : 2020-11-03 DOI: 10.1007/978-3-030-59854-9_8

K. Salako

引用次数: 3

CogQN: A Queueing Model that Captures Human Learning of the User Interfaces of Session-Based Systems CogQN:捕获基于会话系统的用户界面的人类学习的队列模型

International Conference on Quantitative Evaluation of Systems Pub Date : 2020-11-03 DOI: 10.1007/978-3-030-59854-9_10

O. Das, Arindam Das

引用次数: 1