NASA Formal Methods最新文献

Multi-Objective Task Assignment and Multiagent Planning with Hybrid GPU-CPU Acceleration GPU-CPU混合加速下的多目标任务分配与多智能体规划

NASA Formal Methods Pub Date : 2023-05-08 DOI: 10.48550/arXiv.2305.04397

T. Robinson, Guoxin Su

引用次数: 0

A Linear Weight Transfer Rule for Local Search 一种局部搜索的线性权传递规则

NASA Formal Methods Pub Date : 2023-03-27 DOI: 10.48550/arXiv.2303.14894

Md. Solimul Chowdhury, Cayden Codel, Marijn J. H. Heule

{"title":"A Linear Weight Transfer Rule for Local Search","authors":"Md. Solimul Chowdhury, Cayden Codel, Marijn J. H. Heule","doi":"10.48550/arXiv.2303.14894","DOIUrl":"https://doi.org/10.48550/arXiv.2303.14894","url":null,"abstract":"The Divide and Distribute Fixed Weights algorithm (ddfw) is a dynamic local search SAT-solving algorithm that transfers weight from satisfied to falsified clauses in local minima. ddfw is remarkably effective on several hard combinatorial instances. Yet, despite its success, it has received little study since its debut in 2005. In this paper, we propose three modifications to the base algorithm: a linear weight transfer method that moves a dynamic amount of weight between clauses in local minima, an adjustment to how satisfied clauses are chosen in local minima to give weight, and a weighted-random method of selecting variables to flip. We implemented our modifications to ddfw on top of the solver yalsat. Our experiments show that our modifications boost the performance compared to the original ddfw algorithm on multiple benchmarks, including those from the past three years of SAT competitions. Moreover, our improved solver exclusively solves hard combinatorial instances that refute a conjecture on the lower bound of two Van der Waerden numbers set forth by Ahmed et al. (2014), and it performs well on a hard graph-coloring instance that has been open for over three decades.","PeriodicalId":436677,"journal":{"name":"NASA Formal Methods","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121957303","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

Automata-Based Software Model Checking of Hyperproperties 基于自动机的超属性软件模型检测

NASA Formal Methods Pub Date : 2023-03-26 DOI: 10.48550/arXiv.2303.14796

B. Finkbeiner, Hadar Frenkel, Jana Hofmann, Jan-Luca Lohse

引用次数: 0

Strategy Synthesis in Markov Decision Processes Under Limited Sampling Access 有限采样访问下马尔可夫决策过程的策略综合

NASA Formal Methods Pub Date : 2023-03-22 DOI: 10.48550/arXiv.2303.12718

C. Baier, Clemens Dubslaff, Patrick Wienhöft, S. Kiebel

引用次数: 0

Formalizing Piecewise Affine Activation Functions of Neural Networks in Coq Coq中神经网络分段仿射激活函数的形式化

NASA Formal Methods Pub Date : 2023-01-30 DOI: 10.48550/arXiv.2301.12893

A. Aleksandrov, Kim Völlinger

引用次数: 1

Conservative Safety Monitors of Stochastic Dynamical Systems 随机动力系统的保守安全监测

NASA Formal Methods Pub Date : 2023-01-27 DOI: 10.48550/arXiv.2301.11330

Matthew Cleaveland, I. Ruchkin, O. Sokolsky, Insup Lee

引用次数: 1

Open- and Closed-Loop Neural Network Verification using Polynomial Zonotopes 基于多项式带拓扑的开闭环神经网络验证

NASA Formal Methods Pub Date : 2022-07-06 DOI: 10.1007/978-3-031-33170-1_2

Niklas Kochdumper, Christian Schilling, M. Althoff, Stanley Bak

引用次数: 15

More Programming Than Programming: Teaching Formal Methods in a Software Engineering Programme 与其说是编程，不如说是编程:在软件工程程序中教授形式化方法

NASA Formal Methods Pub Date : 2022-05-02 DOI: 10.48550/arXiv.2205.00787

J. Noble, David Streader, Isaac Oscar Gariano, Miniruwani Samarakoon

引用次数: 0

Towards Better Test Coverage: Merging Unit Tests for Autonomous Systems 迈向更好的测试覆盖率:合并自治系统的单元测试

NASA Formal Methods Pub Date : 2022-04-06 DOI: 10.48550/arXiv.2204.02541

Josefine B. Graebener, Apurva Badithela, R. Murray

{"title":"Towards Better Test Coverage: Merging Unit Tests for Autonomous Systems","authors":"Josefine B. Graebener, Apurva Badithela, R. Murray","doi":"10.48550/arXiv.2204.02541","DOIUrl":"https://doi.org/10.48550/arXiv.2204.02541","url":null,"abstract":"We present a framework for merging unit tests for autonomous systems. Typically, it is intractable to test an autonomous system for every scenario in its operating environment. The question of whether it is possible to design a single test for multiple requirements of the system motivates this work. First, we formally define three attributes of a test: a test specification that characterizes behaviors observed in a test execution, a test environment, and a test policy. Using the merge operator from contract-based design theory, we provide a formalism to construct a merged test specification from two unit test specifications. Temporal constraints on the merged test specification guarantee that non-trivial satisfaction of both unit test specifications is necessary for a successful merged test execution. We assume that the test environment remains the same across the unit tests and the merged test. Given a test specification and a test environment, we synthesize a test policy filter using a receding horizon approach, and use the test policy filter to guide a search procedure (e.g. Monte-Carlo Tree Search) to find a test policy that is guaranteed to satisfy the test specification. This search procedure finds a test policy that maximizes a pre-defined robustness metric for the test while the filter guarantees a test policy for satisfying the test specification. We prove that our algorithm is sound. Furthermore, the receding horizon approach to synthesizing the filter ensures that our algorithm is scalable. Finally, we show that merging unit tests is impactful for designing efficient test campaigns to achieve similar levels of coverage in fewer test executions. We illustrate our framework on two self-driving examples in a discrete-state setting.","PeriodicalId":436677,"journal":{"name":"NASA Formal Methods","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122500380","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

NNLander-VeriF: A Neural Network Formal Verification Framework for Vision-Based Autonomous Aircraft Landing NNLander-VeriF:一种基于视觉的自主飞机着陆神经网络形式化验证框架

NASA Formal Methods Pub Date : 2022-03-29 DOI: 10.48550/arXiv.2203.15841

Ulices Santa Cruz, Yasser Shoukry

{"title":"NNLander-VeriF: A Neural Network Formal Verification Framework for Vision-Based Autonomous Aircraft Landing","authors":"Ulices Santa Cruz, Yasser Shoukry","doi":"10.48550/arXiv.2203.15841","DOIUrl":"https://doi.org/10.48550/arXiv.2203.15841","url":null,"abstract":". In this paper, we consider the problem of formally verifying a Neural Network (NN) based autonomous landing system. In such a system, a NN controller processes images from a camera to guide the aircraft while approaching the runway. A central challenge for the safety and liveness veriﬁcation of vision-based closed-loop systems is the lack of mathematical models that captures the relation between the system states (e.g., position of the aircraft) and the images processed by the vision-based NN controller. Another challenge is the limited abilities of state-of-the-art NN model checkers. Such model checkers can reason only about simple input-output robustness properties of neural networks. This limitation creates a gap between the NN model checker abilities and the need to verify a closed-loop system while considering the aircraft dynamics, the perception components, and the NN controller. To this end, this paper presents NNLander-VeriF, a framework to verify vision-based NN controllers used for autonomous landing. NNLander-VeriF addresses the challenges above by exploiting geometric models of perspective cameras to obtain a mathematical model that captures the relation between the aircraft states and the inputs to the NN controller. By converting this model into a NN (with manually assigned weights) and composing it with the NN controller, one can capture the relation between aircraft states and control actions using one augmented NN. Such an augmented NN model leads to a natural encoding of the closed-loop veriﬁcation into several NN robustness queries, which state-of-the-art NN model checkers can handle. Finally, we evaluate our framework to formally verify the properties of a trained NN and we show its eﬃciency. LiDAR scanners and cameras. These data","PeriodicalId":436677,"journal":{"name":"NASA Formal Methods","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128112292","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: 12