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With the emergence of smart technology and automated systems in today’s world, artificial intelligence (AI) is being incorporated into an array of professions. The aviation and aerospace industry, specifically, is a field that has seen the successful implementation of early stages of automation in daily flight operations through flight management systems and autopilot. However, the effectiveness of aviation systems and the provision of flight safety still depend primarily upon the reliability of aviation specialists and human decision making. The Handbook of Research on Artificial Intelligence Applications in the Aviation and Aerospace Industries is a pivotal reference source that explores best practices for AI implementation in aviation to enhance security and the ability to learn, improve, and predict. While highlighting topics such as computer-aided design, automated systems, and human factors, this publication explores the enhancement of global aviation security as well as the methods of modern information systems in the aeronautics industry. This book is ideally designed for pilots, scientists, engineers, aviation operators, air crash investigators, teachers, academicians, researchers, and students seeking current research on the application of AI in the field of aviation.
The four-volume set LNAI 6276--6279 constitutes the refereed proceedings of the 14th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2010, held in Cardiff, UK, in September 2010. The 272 revised papers presented were carefully reviewed and selected from 360 submissions. They present the results of high-quality research on a broad range of intelligent systems topics.
Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks.
Developing today's complex systems requires more than just good software engineering solutions. Many are faced with complex systems projects, incomplete or inaccurate requirements, canceled projects, or cost overruns, and have their systems' users in revolt and demanding more. Others want to build user-centric systems, but fear managing the process. This book describes an approach that brings the engineering process together with human performance engineering and business process reengineering. The result is a manageable user-centered process for gathering, analyzing, and evaluating requirements that can vastly improve the success rate in the development of medium-to-large size systems and applications. Unlike some texts that are primarily conceptual, this volume provides guidelines, "how-to" information, and examples, enabling the reader to quickly apply the process and techniques to accomplish the following goals: * define high quality requirements, * enhance productive client involvement, * help clients maintain competitiveness, * ensure client buy-in and support throughout the process, * reduce missing functionality and corrections, and * improve user satisfaction with systems. This volume clearly details the role of user-centered requirements and knowledge acquisition within Scenario-Based Engineering Process (SEP) and identifies SEP products and artifacts. It assists project personnel in planning and managing effective requirements activities, including managing risks, avoiding common problems with requirements elicitation, organizing project participants and tools, and managing the logistics. Guidelines are provided for the following: selecting the right individual and group techniques to elicit scenarios and requirements from users; subject matter experts, or other shareholders; and ensuring engineers or analysts have the necessary skills.
This two-volume set constitutes the refereed proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE-98, held in Benicassim, Castellon, Spain, in June 1998.The two volumes present a total of 187 revised full papers selected from 291 submissions. In accordance with the conference, the books are devoted to new methodologies, knowledge modeling and hybrid techniques. The papers explore applications from virtually all subareas of AI including knowledge-based systems, fuzzyness and uncertainty, formal reasoning, neural information processing, multiagent systems, perception, robotics, natural language processing, machine learning, supervision and control systems, etc..