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A unified formulation of computer-aided, multi-task, decision making is presented. Strategy for the allocation of decision making responsibility between human and computer is developed. The plans of a flight management systems are studied. A model based on the queueing theory was implemented.
A full mission simulation of a civil air transport scenario that had two levels of workload was used to observe the actions of the crews and the basic aircraft parameters and to record heart rates. The results showed that the number of errors was very variable among crews but the mean increased in the higher workload case. The increase in errors was not related to rise in heart rate but was associated with vigilance times as well as the days since the last flight. The recorded data also made it possible to investigate decision time and decision order. These also varied among crews and seemed related to the ability of captains to manage the resources available to them on the flight deck.
An efficient and accurate procedure for estimating inventories from remote sensing scenes is presented. In place of the conventional and expensive full dimensional Bayes decision rule, a one-dimensional feature extraction and classification technique was employed. It is shown that this efficient decision rule can be used to develop unbiased inventory estimates and that for large sample sizes typical of satellite derived remote sensing scenes, resulting accuracies are comparable or superior to more expensive alternative procedures. Mathematical details of the procedure are provided in the body of the report and in the appendix. Results of a numerical simulation of the technique using statistics obtained from an observed LANDSAT scene are included. The simulation demonstrates the effectiveness of the technique in computing accurate inventory estimates.
In modern guidance and control systems, the human as manager, supervisor, decision maker, problem solver and trouble shooter, often has to cope with a marginal mental workload. To improve this situation, computers should be used to reduce the operator from mental stress. This should not solely be done by increased automation, but by a reasonable sharing of tasks in a human-computer team, where the computer supports the human intelligence. Recent developments in this area are summarized. It is shown that interactive support of operator by intelligent computer is feasible during information evaluation, decision making and problem solving. The applied artificial intelligence algorithms comprehend pattern recognition and classification, adaptation and machine learning as well as dynamic and heuristic programming. Elementary examples are presented to explain basic principles.
Decisions requiring passenger comfort technology were shown to depend on: the relationship between comfort and other factors (e.g., cost, urgency, alternate modes) in traveler acceptance of the systems, serving a selected market require technology to quantify effects of comfort versus offsetting factors in system acceptance. Public predict the maximum percentage of travelers who willingly accept the overall comfort of any trip ride. One or the other of these technology requirements apply to decisions on system design, operation and maintenance.
To solve the problems inherent in working with sequential decision processes, it is proposed to (1) utilize concepts of dominance through bounding in the decision processes (DP) formalism to reduce the amount of computing required. This advocates the marrying of DP recursion and Branch-and-Bound methodology; and (2) relax the requirement of strict optimality in the search over the state space, and be content with a tolerable error.
The need to teach informational writing as a decision-making process is discussed. Situational analysis, its relationship to decisions in writing, and the need for relevant assignments are considered. Teaching students to ask the right questions is covered. The need to teach writing responsiveness is described. Three steps to get started and four teaching techniques are described. The information needs of the 'expert' and the 'manager' are contrasted.
The development and testing of an analysis procedure which was developed to improve the consistency and objectively of crop identification using Landsat data is described. The procedure was developed to identify corn and soybean crops in the U.S. corn belt region. The procedure consists of a series of decision points arranged in a tree-like structure, the branches of which lead an analyst to crop labels. The specific decision logic is designed to maximize the objectively of the identification process and to promote the possibility of future automation. Significant results are summarized.
Human decision making in multiple process monitoring situations is considered. It is proposed that human decision making in many multiple process monitoring situations can be modeled in terms of the human's detection of process related events and his allocation of attention among processes once he feels event have occurred. A mathematical model of human event detection and attention allocation performance in multiple process monitoring situations is developed. An assumption made in developing the model is that, in attempting to detect events, the human generates estimates of the probabilities that events have occurred. An elementary pattern recognition technique, discriminant analysis, is used to model the human's generation of these probability estimates. The performance of the model is compared to that of four subjects in a multiple process monitoring situation requiring allocation of attention among processes.
The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming.
A central problem to parallel processing is the determination of an effective partitioning of workload to processors. The effectiveness of any given partition is dependent on the stochastic nature of the workload. The problem of determining when and if the stochastic behavior of the workload has changed enough to warrant the calculation of a new partition is treated. The problem is modeled as a Markov decision process, and an optimal decision policy is derived. Quantification of this policy is usually intractable. A heuristic policy which performs nearly optimally is investigated empirically. The results suggest that the detection of change is the predominant issue in this problem.
An experiment conducted using a head-up display (HUD) suggests that the demonstrated superiority of the HUD over a conventional instrument panel stems from its superior layout of information. A HUD display presents instrument-panel information to pilots in such a way that the symbols appear as a virtual image at optical infinity superimposed on the landscape. In the experiment conducted, the luminance of the display symbology and its angle subtended at the eye remained fixed, while optical distance and gaze angle were varied. Concomitant measures of eye movements, eye accommodative state, and decision-making time concerning airspeed, altitude and runway condition were obtained. It is found that, while looking straight ahead, at zero diopters, the HUD shortens decision time by 80 to 90 msec, not statistically significant at the 0.05 (slope of diopter/gaze interaction) level. The question of a cognitive overload induced by the luminous symbols of the HUD is subsequently addressed.
This paper analyzes a NASA Convair 990 (CV-990) accident with emphasis on rejected-takeoff (RTO) decision making, training, procedures, and accident statistics. The NASA Aircraft Accident Investigation Board was somewhat perplexed that an aircraft could be destroyed as a result of blown tires during the takeoff roll. To provide a better understanding of tire failure RTO's, The Board obtained accident reports, Federal Aviation Administration (FAA) studies, and other pertinent information related to the elements of this accident. This material enhanced the analysis process and convinced the Accident Board that high-speed RTO's in transport aircraft should be given more emphasis during pilot training. Pilots should be made aware of various RTO situations and statistics with emphasis on failed-tire RTO's. This background information could enhance the split-second decision-making process that is required prior to initiating an RTO.
A game-matrix approach is used to generate intelligent maneuvering decisions for nap-of-the-earth one-on-one helicopter air combat over hilly terrain. Decisions are made in real time using the AUTOMAN program by comparing scores based upon the predicted orientation, range, velocity, and terrain clearance for various maneuver combinations of both aircraft. The program has been demonstrated at the NASA Ames Vertical Motion Simulator, providing manned helicopter simulations. Sample trajectories from a stand-alone version of the program designed for nonpiloted simulations demonstrate that the present automated maneuvering logic generates some of the tactics employed by experienced pilots in flight tests. The program also has application to fixed-wing aircraft simulations.
In recent years there has been increasing interest in applying the computer based problem solving techniques of Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS) to analyze extremely complex problems. A conceptual framework is developed for successfully integrating these three techniques. First, the fields of AI, OR, and DSS are defined and the relationships among the three fields are explored. Next, a comprehensive adaptive design methodology for AI and OR modeling within the context of a DSS is described. These observations are made: (1) the solution of extremely complex knowledge problems with ill-defined, changing requirements can benefit greatly from the use of the adaptive design process, (2) the field of DSS provides the focus on the decision making process essential for tailoring solutions to these complex problems, (3) the characteristics of AI, OR, and DSS tools appears to be converging rapidly, and (4) there is a growing need for an interdisciplinary AI/OR/DSS education.
The Decision Support Problem Technique for unified design, manufacturing and maintenance is being developed at the Systems Design Laboratory at the University of Houston. This involves the development of a domain-independent method (and the associated software) that can be used to process domain-dependent information and thereby provide support for human judgment. In a computer assisted environment, this support is provided in the form of optimal solutions to Decision Support Problems.
The role that artificial intelligence/expert systems technologies play in the development and implementation of effective decision support systems is illustrated. A recently developed prototype system for supporting the scheduling of subsystems and payloads/experiments for NASA's Space Station program is presented and serves to highlight various concepts. The potential integration of knowledge based systems and decision support systems which has been proposed in several recent articles and presentations is illustrated.
Currently there is a great demand for mental workload evaluation in the course of system design and modification. In light of this demand, a microprocessor-based decision support system has been created called WC FIELDE: Workload Consultant for FIELD Evaluation. The system helps the user select workload measures appropriate to his or her application from the large pool of currently available techniques. Both novices and those with some workload experience may benefit from using WC FIELDE, since the system's operation is entirely transparent and all rules involved in the decision process are available for the user to examine. WC FIELDE recommends several assessment methodologies in decreasing order of appropriateness, and provides additional information on each measure at the end of the program in the form of text files.