A Comparative Study of Novice vs. Experienced Pilots Utilizing Cockpit Models Applied to the Multi Function Display System

Based on previous research findings in the expert/novice area, this thesis suggests that how people approach and solve problems largely depend on their experience level. The literature supports that experts tend to prioritize their actions differently than their novice counterparts as they negotiate their way through various critical flight scenarios. In this study we assume this difference is, in part, related to the fact that experts have a more complete understanding/perception of the overall situation than do novices. Psychologists often refer to this understanding as a more complete mental model. The purpose of this study was to evaluate if the proposed format of the ICIS will effect performance of novice pilot\u27s as compared experienced pilots. In order to establish the potential difference in expert/novice problem-solving, a perception task was assigned to the participants. Specifically, the task was to rank pertinent flight information according to how the pilot perceives the items related to the scenario which she is presented via the display program. As is suggested throughout the literature aligned to this study, the results indicate that as a person gains experience ranking relevance differ. The perception test developed for this study was used to establish this assumed difference. Further indicated in the literature by Ericsson and Chamess (1994), differences in expertise provide the largest and most reliable differences in performance between individuals. Therefore, in order to test the claimed effectiveness of the new touch-screen display, the research focused on performance measures. In this study both groups were asked to solve similar flight scenarios solely with the help of the ICIS display. The performance of the participants was based on their ability to find flight information on the screen, the amount of keystrokes they used, and the time they needed to solve the problem. It was assumed the ICIS\u27s highly structured organization and would effectively guide the user through the problem-solving process, with a significant improvement in human-computer interaction graphical touch-screen display performance. Cognitive load theory suggests that available information should support structures which eliminate any excess load on working memory in order to enhance learning. The literature further explains that as individuals store learned information in long-term memory, this process reduces the burden on working memory by allowing multiple elements of information to be treated as a single or unified response (Kalyuga, Chandler &Sweller, 1998). Similar, in cognitive terms, to chunking, this layering is thought to provide benefit to the novices and experts alike in navigating computer interfaces. As a basis for design effectiveness, this study tested hypotheses using the ICIS. Seeking to support the notion that several layers of information on one display provides equal reduction of working memory, regardless of the participants\u27 level of flight experience. This study supports the assumption that layering provides benefit to the user of the ICIS, regardless of experience level

Human factors aspects of air traffic control

An overview of human factors problems associated with the operation of present and future air traffic control systems is presented. A description is included of those activities and tasks performed by air traffic controllers at each operational position within the present system. Judgemental data obtained from controllers concerning psychological dimensions related to these tasks and activities are also presented. The analysis includes consideration of psychophysiological dimensions of human performance. The role of the human controller in present air traffic control systems and his predicted role in future systems is described, particularly as that role changes as the result of the system's evolution towards a more automated configuration. Special attention is directed towards problems of staffing, training, and system operation. A series of ten specific research and development projects are recommended and suggested work plans for their implementation are included

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

Introduction of programmable logic controller in industrial engineering curriculum

Recent trends in process control and industrial automation scenarios have resulted in the emergence of many pioneering techniques that have revolutionized the manufacturing industry. In order to maintain quality and precision, advances have been associated with the increasing use of microprocessors in process control applications. Most of the industrial process control systems utilize Programmable Logic Controllers (PLC). Also due to the increase in internet usage and recent innovations in PLC software, remote monitoring and PLC control of process through the internet is also a recent trend. This thesis presents course/lab material for integration in the Industrial Engineering curriculum. The course/lab content was designed to improve the student\u27s knowledge and to broaden the industrial engineering curriculum at West Virginia University (WVU). This thesis proposes the use of inexpensive T100MD+ PLCs. A traffic light control system was developed to introduce the fundamental concepts of Boolean algebra and real-time control. A series of control exercises can be carried on the traffic light system. A temperature sensitive system was also developed. Students can test various PID control strategies on this hardware/software platform. Students will also have the ability to control the process via the internet

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

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

Applications of Artificial Intelligence in Military Training Simulation

This report is a survey of Artificial Intelligence (AI) technology contributions to military training. It provides an overview of military training simulation and a review of instructional problems and challenges which can be addressed by AI. The survey includes current as well as potential applications of AI, with particular emphasis on design and system integration issues. Applications include knowledge and skills training in strategic planning and decision making, tactical warfare operations, electronics maintenance and repair, as well as computer-aided design of training systems. The report describes research contributions in the application of AI technology to the training world, and it concludes with an assessment of future research directions in this area

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Big data assisted CRAN enabled 5G SON architecture

The recent development of Big Data, Internet of Things (IoT) and 5G network technology offers a plethora of opportunities to the IT industry and mobile network operators. 5G cellular technology promises to offer connectivity to massive numbers of IoT devices while meeting low-latency data transmission requirements. A deficiency of the current 4G networks is that the data from IoT devices and mobile nodes are merely passed on to the cloud and the communication infrastructure does not play a part in data analysis. Instead of only passing data on to the cloud, the system could also contribute to data analysis and decision-making. In this work, a Big Data driven self-optimized 5G network design is proposed using the knowledge of emerging technologies CRAN, NVF and SDN. Also, some technical impediments in 5G network optimization are discussed. A case study is presented to demonstrate the assistance of Big Data in solving the resource allocation problem