A novel deep multi-agent reinforcement learning framework is proposed to identify and resolve conflicts among a variable number of aircraft in a high-density, stochastic, and dynamic sector. Currently the sector capacity is constrained by human air traffic controller's cognitive limitation. We investigate the feasibility of a new concept (autonomous separation assurance) and a new approach to push the sector capacity above human cognitive limitation. We propose the concept of using distributed vehicle autonomy to ensure separation, instead of a centralized sector air traffic controller. Our proposed framework utilizes Proximal Policy Optimization (PPO) that we modify to incorporate an attention network. This allows the agents to have access to variable aircraft information in the sector in a scalable, efficient approach to achieve high traffic throughput under uncertainty. Agents are trained using a centralized learning, decentralized execution scheme where one neural network is learned and shared by all agents. The proposed framework is validated on three challenging case studies in the BlueSky air traffic control environment. Numerical results show the proposed framework significantly reduces offline training time, increases performance, and results in a more efficient policy.
Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning
Department of Mechanical and Aerospace Engineering, George Washington University（机械与航空航天工程系，乔治华盛顿大学）；Department of Electrical and Computer Engineering, Purdue University（电气与计算机工程系，普渡大学）
Algorithmic assurances from advanced autonomous systems assist human users in understanding, trusting, and using such systems appropriately. Designing these systems with the capacity of assessing their own capabilities is one approach to creating an algorithmic assurance. The idea of `machine self-confidence' is introduced for autonomous systems. Using a factorization based framework for self-confidence assessment, one component of self-confidence, called `solver-quality', is discussed in the context of Markov decision processes for autonomous systems. Markov decision processes underlie much o…
Agentic AI for Ultra-Modern Networks: Multi-Agent Framework for RAN Autonomy and Assurance
Samsung Research（三星研究院）；The Royal Melbourne Institute of Technology（皇家墨尔本理工大学）；Dublin City University（都柏林城市大学）；Munster Technological University（芒斯特理工大学）
Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance. Many of these systems, in domains such as healthcare , automotive and manufacturing, exhibit high degrees of autonomy and are safety critical. Establishing justified confidence in ML forms a core part of the safety case for these systems. In this document we introduce a methodology for the Assurance of Machine Learning for use in Autonomous Systems (AMLAS). AMLAS comprises a set of safety case patterns and a process for (1) systematically integrating sa…
This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines.
Norwegian University of Science and Technology（挪威科技大学）；TrønderEnergi（特伦德拉格能源公司）
Deep reinforcement learning has led to numerous notable results in robotics. However, deep neural networks (DNNs) are unintuitive, which makes it difficult to understand their predictions and strongly limits their potential for real-world applications due to economic, safety, and assurance reasons. To remedy this problem, a number of explainable AI methods have been presented, such as SHAP and LIME, but these can be either be too costly to be used in real-time robotic applications or provide only local explanations. In this paper, the main contribution is the use of a linear model tree (LMT) t…
Iterative Audit Convergence in LLM-Managed Multi-Agent Systems: A Case Study in Prompt-Engineering Quality Assurance
Swift (North) AI Lab, The Swift Group, LLC, Maryland, USA（Swift（北）AI实验室，The Swift Group LLC，马里兰州，美国）；Capitol Technology University, Laurel, MD 20708, USA（Capitol技术大学，Laurel，马里兰州20708，美国）
Synergistic Simplex: Cooperative Runtime Assurance for Safety-Critical Autonomous Systems
University of Illinois Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）；Center for Scientific Innovation and Education (CSIE)（科学创新与教育中心）；California Institute of Technology (Caltech)（加州理工学院）；Mercer University（梅森大学）
Vanderbilt University（范德堡大学）；Institute for Software Integrated Systems（软件集成系统研究所）
Autonomous Cyber-Physical Systems must often operate under uncertainties like sensor degradation and shifts in the operating conditions, which increases its operational risk. Dynamic Assurance of these systems requires designing runtime safety components like Out-of-Distribution detectors and risk estimators, which require labeled data from different operating modes of the system that belong to scenes with adverse operating conditions, sensors, and actuator faults. Collecting real-world data of these scenes can be expensive and sometimes not feasible. So, scenario description languages with sa…
The European Machinery Directive and related harmonized standards do consider that software is used to generate safety-relevant behavior of the machinery but do not consider all kinds of software. In particular, software based on machine learning (ML) are not considered for the realization of safety-relevant behavior. This limits the introduction of suitable safety concepts for autonomous mobile robots and other autonomous machinery, which commonly depend on ML-based functions. We investigated this issue and the way safety standards define safety measures to be implemented against software fau…
Recent advances in machine learning technologies and sensing have paved the way for the belief that safe, accessible, and convenient autonomous vehicles may be realized in the near future. Despite tremendous advances within this context, fundamental challenges around safety and reliability are limiting their arrival and comprehensive adoption. Autonomous vehicles are often tasked with operating in dynamic and uncertain environments. As a result, they often make use of highly complex components, such as machine learning approaches, to handle the nuances of sensing, actuation, and control. While…
University of Liverpool（利物浦大学）；Heriot-Watt University（赫瑞-瓦特大学）；Defence Science and Technology Laboratory（国防科学技术实验室）；Imperial College London（帝国理工学院）
The increasing use of Machine Learning (ML) components embedded in autonomous systems -- so-called Learning-Enabled Systems (LESs) -- has resulted in the pressing need to assure their functional safety. As for traditional functional safety, the emerging consensus within both, industry and academia, is to use assurance cases for this purpose. Typically assurance cases support claims of reliability in support of safety, and can be viewed as a structured way of organising arguments and evidence generated from safety analysis and reliability modelling activities. While such assurance activities ar…
Advances in the field of Machine Learning and Deep Neural Networks (DNNs) has enabled rapid development of sophisticated and autonomous systems. However, the inherent complexity to rigorously assure the safe operation of such systems hinders their real-world adoption in safety-critical domains such as aerospace and medical devices. Hence, there is a surge in interest to explore the use of advanced mathematical techniques such as formal methods to address this challenge. In fact, the initial results of such efforts are promising. Along these lines, we propose the use of quantifier elimination (…
Carnegie Mellon University（卡内基梅隆大学）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；Boeing（波音公司）
As autonomy becomes prevalent in many applications, ranging from recommendation systems to fully autonomous vehicles, there is an increased need to provide safety guarantees for such systems. The problem is difficult, as these are large, complex systems which operate in uncertain environments, requiring data-driven machine-learning components. However, learning techniques such as Deep Neural Networks, widely used today, are inherently unpredictable and lack the theoretical foundations to provide strong assurance guarantees. We present a compositional approach for the scalable, formal verificat…
Ovarian Cancer Data Analysis using Deep Learning: A Systematic Review from the Perspectives of Key Features of Data Analysis and AI Assurance
School of Health and Life Sciences, Teesside University（提赛德大学健康与生命科学学院）；School of Computing, Engineering and Digital Technologies, Teesside University（提赛德大学计算、工程与数字技术学院）
