Longitudinal flying qualities criteria for single-pilot instrument flight operations

Modern estimation and control theory, flight testing, and statistical analysis were used to deduce flying qualities criteria for General Aviation Single Pilot Instrument Flight Rule (SPIFR) operations. The principal concern is that unsatisfactory aircraft dynamic response combined with high navigation/communication workload can produce problems of safety and efficiency. To alleviate these problems. The relative importance of these factors must be determined. This objective was achieved by flying SPIFR tasks with different aircraft dynamic configurations and assessing the effects of such variations under these conditions. The experimental results yielded quantitative indicators of pilot's performance and workload, and for each of them, multivariate regression was applied to evaluate several candidate flying qualities criteria

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

Third CLIPS Conference Proceedings, volume 1

Expert systems are computed programs which emulate human expertise in well defined problem domains. The potential payoff from expert systems is high: valuable expertise can be captured and preserved, repetitive and/or mundane tasks requiring human expertise can be automated, and uniformity can be applied in decision making processes. The C Language Integrated Production Systems (CLIPS) is an expert system building tool, developed at the Johnson Space Center, which provides a complete environment for the development and delivery of rule and/or object based expert systems. CLIPS was specifically designed to provide a low cost option for developing and deploying expert system applications across a wide range of hardware platforms. The development of CLIPS has helped to improve the ability to deliver expert systems technology throughout the public and private sectors for a wide range of applications and diverse computing environments

Efficient algorithms for risk-averse air-ground rendezvous missions

Demand for fast and inexpensive parcel deliveries in urban environments has risen considerably in recent years. A framework is envisioned to enforce efficient last-mile delivery in urban environments by leveraging a network of ride-sharing vehicles, where Unmanned Aerial Systems (UASs) drop packages on said vehicles, which then cover the majority of the distance before final aerial delivery. By combining existing networks we show that the range and efficiency of UAS-based delivery logistics are greatly increased. This approach presents many engineering challenges, including the safe rendezvous of both agents: the UAS and the human-operated ground vehicle. This dissertation presents tools that guarantee risk-optimal rendezvous between the two vehicles. We present mechanical and algorithmic tools that achieve this goal. Mechanically, we develop a novel aerial manipulator and controller that improves in-flight stability during the pickup and drop-off of packages. At a higher level and the core of this dissertation, we present planning algorithms that mitigate risks associated with human behavior at the longest time scales. First, we discuss the downfalls of traditional approaches. In aerial manipulation, we show that popular anthropomorphic designs are unsuitable for flying platforms, which we tackle with a combination of lightweight design of a delta-type parallel manipulator, and L1 adaptive control with feedforward. In planning algorithms, we present evidence of erratic driver behavior that can lead to catastrophic failures. Such a failure occurs when the UAS depletes its resource (battery, fuel) and has to crash land on an unplanned location. This is particularly dangerous in urban environments where population density is high, and the probability of harming a person or property in the event of a failure is unsafe. Studies have shown that two types of erratic behavior are common: speed variation and route choice. Speed variation refers to a common disregard for speed limits combined with different levels of comfort per driver. Route choice is conscious, unconscious, or purely random action of deviating from a prescribed route. Route choice uncertainty is high dimensional and complex both in space and time. Dealing with these types of uncertainty is important to many fields, namely traffic flow modeling. The critical difference to our interpretation is that we frame them in a motion planning framework. As such, we assume each driver has an unknown stochastic model for their behavior, a model that we aim to approximate through different methods. We aim to guarantee safety by quantifying motion planning risks associated with erratic human behavior. Only missions that plan on using all of the UAS's resources have inherent risk. We postulate that if we have a high assurance of success, any mission can be made to use more resources and be more efficient for the network by completing its objective faster. Risk management is addressed at three different scales. First, we focus on speed variation. We approach this problem with a combination of risk-averse Model Predictive Control (MPC) and Gaussian Processes. We use risk as a measure of the probability of success, centered around estimated future driver position. Several risk measures are discussed and CVaR is chosen as a robust measure for this problem. Second we address local route choice. This is route uncertainty for a single driver in some region of space. The primary challenge is the loss of gradient for the MPC controller. We extend the previous approach with a cross-entropy stochastic optimization algorithm that separates gradient-based from gradient-free optimization problems within the planner. We show that this approach is effective through a variety of numerical simulations. Lastly, we study a city-wide problem of estimating risk among several available drivers. We use real-world data combined with synthetic experiments and Deep Neural Networks (DNN) to produce an accurate estimator. The main challenges in this approach are threefold: DNN architecture, driver model, and data processing. We found that this learning problem suffers from vanishing gradients and numerous local minima, which we address with modern self-normalization techniques and mean-adjusted CVaR. We show the model's effectiveness in four scenarios of increasing complexity and propose ways of addressing its shortcomings

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

The AI Revolution: Opportunities and Challenges for the Finance Sector

This report examines Artificial Intelligence (AI) in the financial sector, outlining its potential to revolutionise the industry and identify its challenges. It underscores the criticality of a well-rounded understanding of AI, its capabilities, and its implications to effectively leverage its potential while mitigating associated risks. The potential of AI potential extends from augmenting existing operations to paving the way for novel applications in the finance sector. The application of AI in the financial sector is transforming the industry. Its use spans areas from customer service enhancements, fraud detection, and risk management to credit assessments and high-frequency trading. However, along with these benefits, AI also presents several challenges. These include issues related to transparency, interpretability, fairness, accountability, and trustworthiness. The use of AI in the financial sector further raises critical questions about data privacy and security. A further issue identified in this report is the systemic risk that AI can introduce to the financial sector. Being prone to errors, AI can exacerbate existing systemic risks, potentially leading to financial crises. Regulation is crucial to harnessing the benefits of AI while mitigating its potential risks. Despite the global recognition of this need, there remains a lack of clear guidelines or legislation for AI use in finance. This report discusses key principles that could guide the formation of effective AI regulation in the financial sector, including the need for a risk-based approach, the inclusion of ethical considerations, and the importance of maintaining a balance between innovation and consumer protection. The report provides recommendations for academia, the finance industry, and regulators

Air Traffic Control

Improving air traffic control and air traffic management is currently one of the top priorities of the global research and development agenda. Massive, multi-billion euro programs like SESAR (Single European Sky ATM Research) in Europe and NextGen (Next Generation Air Transportation System) in the United States are on their way to create an air transportation system that meets the demands of the future. Air traffic control is a multi-disciplinary field that attracts the attention of many researchers, ranging from pure mathematicians to human factors specialists, and even in the legal and financial domains the optimization and control of air transport is extensively studied. This book, by no means intended to be a basic, formal introduction to the field, for which other textbooks are available, includes nine chapters that demonstrate the multi-disciplinary character of the air traffic control domain

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining