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Cyber-physical systems (CPS) involve deeply integrated, tightly coupled computational and physical components. These systems, spanning multiple scientific and technological domains, are highly complex and pose several fundamental challenges. They are also critically important to society’s advancement and security. The design and deployment of the adaptable, reliable CPS of tomorrow requires the development of a basic science foundation, synergistically drawing on various branches of engineering, mathematics, computer science, and domain specific knowledge.   This book brings together 19 invited papers presented at the Workshop on Control of Cyber-Physical Systems, hosted by the Department of Electrical & Computer Engineering at The Johns Hopkins University in March 2013. It highlights the central role of control theory and systems thinking in developing the theory of CPS, in addressing the challenges of cyber-trust and cyber-security, and in advancing emerging cyber-physical applications ranging from smart grids to smart buildings, cars and robotic systems.  .

Applied physical engineering --- Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- systeemtheorie --- controleleer --- KI (kunstmatige intelligentie) --- systeembeheer --- ingenieurswetenschappen --- AI (artificiële intelligentie)

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This report presents a framework for the development of metrics—and a method for scoring them—that indicates how well a U.S. Air Force mission or system is expected to perform in a cyber-contested environment. These metrics are developed so as to be suitable for informing acquisition decisions during all stages of weapon systems' life cycles. There are two types of cyber metrics: working-level metrics to counter an adversary's cyber operations and institutional-level metrics to capture any cyber-related organizational deficiencies. The cyber environment is dynamic and complex, the threat is ubiquitous (in peacetime and wartime, deployed and at home), and no set of underlying "laws of nature" govern the cyber realm. A fruitful approach is to define cyber metrics in the context of a two-player cyber game between Red (the attacking side) and Blue (the side trying to ensure a mission). The framework helps, in part, to reveal where strengths in one area might partially offset weaknesses in another. Additional discussions focus on how those metrics can be scored in ways that are useful for supporting decisions. The metrics are aimed at supporting program offices and authorizing officials in risk management and in defining requirements, both operational requirements as well as the more detailed requirements for system design used in contracts, the latter often referred to as derived requirements.

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RAND researchers explored the capabilities and limitations of future weapon systems incorporating artificial intelligence and machine learning (AI/ML) through two wargame experiments. The researchers modified and augmented the rules and engagement statistics used in a commercial tabletop wargame to enable (1) remotely operated and fully autonomous combat vehicles and (2) vehicles with AI/ML–enabled situational awareness to show how the two types of vehicles would perform in company-level engagements between Blue (U.S.) and Red (Russian) forces. Those rules sought to realistically capture the capabilities and limitations of those systems, including their vulnerability to selected enemy countermeasures, such as jamming. Future work could improve the realism of both the gameplay and representation of AI/ML–enabled systems. In this experiment, participants played two games: a baseline game and an AI/ML game. Throughout play in the two game scenarios, players on both sides discussed the capabilities and limitations of the remotely operated and fully autonomous systems and their implications for engaging in combat using such systems. These discussions led to changes in how the systems were employed by the players and observations about which limitations should be mitigated before commanders were likely to accept a system and which capabilities needed to be fully understood by commanders so that systems could be employed appropriately. This research demonstrated how such games, by bringing together operators and engineers, could be used by the requirements and acquisition communities to develop realizable requirements and engineering specifications for AI/ML systems.

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The acquisition process for a new weapon system involves developing a set of technical requirements - a set of statements or models defining what a system should do and how well it should do it - for the system's design to ensure that the system provides the needed operational capability within budget and schedule constraints. However, oversights during this process can result in cost or schedule overruns, unsuitable operational performance, or outright cancellation. The U.S. Department of the Air Force (DAF) asked RAND Project AIR FORCE to develop an approach to help improve DAF's technical requirements development process. To do so, the authors consulted policies and the literature, held discussions with DAF stakeholders and subject-matter experts, conducted two case studies, and assessed various tools that might assist development of technical requirements. This report describes the resulting approach, which has been informed by systems-based methods and tools, and includes an exploration of the applicability and feasibility of one specific emerging hazard-analysis tool: system-theoretic process analysis (STPA).

Systems engineering. --- Engineering mathematics. --- United States. --- Procurement. --- Weapons systems.