An information assistant system for the prevention of tunnel vision in crisis management

In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions

An analysis of the application of AI to the development of intelligent aids for flight crew tasks

This report presents the results of a study aimed at developing a basis for applying artificial intelligence to the flight deck environment of commercial transport aircraft. In particular, the study was comprised of four tasks: (1) analysis of flight crew tasks, (2) survey of the state-of-the-art of relevant artificial intelligence areas, (3) identification of human factors issues relevant to intelligent cockpit aids, and (4) identification of artificial intelligence areas requiring further research

Cognitive Engineering Research Methodology: A Proposed Study of Visualization Analysis Techniques

The rapid development of new sensors and wide-band communications provides the capability to collect enormous amounts of data. An increasing challenge involves how to understand and interpret the data to yield knowledge about evolving situations or threats (e.g., of military situations, state of complex systems, etc.). New visualization tools and techniques are becoming available to support advanced visualization including three-dimensional, full immersion display environments and tools to support novel visualizations. Examples include network system display tools and evolving multi-sensory situation environments. Despite the emergence of such tools, there has been limited systematic test and evaluations to determine the efficacy of such tools for knowledge understanding and decision making. This paper provides an overview of this problem and argues for the need to conduct controlled experiments. A sample experiment is suggested

Applications of agent architectures to decision support in distributed simulation and training systems

This work develops the approach and presents the results of a new model for applying intelligent agents to complex distributed interactive simulation for command and control. In the framework of tactical command, control communications, computers and intelligence (C4I), software agents provide a novel approach for efficient decision support and distributed interactive mission training. An agent-based architecture for decision support is designed, implemented and is applied in a distributed interactive simulation to significantly enhance the command and control training during simulated exercises. The architecture is based on monitoring, evaluation, and advice agents, which cooperate to provide alternatives to the dec ision-maker in a time and resource constrained environment. The architecture is implemented and tested within the context of an AWACS Weapons Director trainer tool. The foundation of the work required a wide range of preliminary research topics to be covered, including real-time systems, resource allocation, agent-based computing, decision support systems, and distributed interactive simulations. The major contribution of our work is the construction of a multi-agent architecture and its application to an operational decision support system for command and control interactive simulation. The architectural design for the multi-agent system was drafted in the first stage of the work. In the next stage rules of engagement, objective and cost functions were determined in the AWACS (Airforce command and control) decision support domain. Finally, the multi-agent architecture was implemented and evaluated inside a distributed interactive simulation test-bed for AWACS Vv\u27Ds. The evaluation process combined individual and team use of the decision support system to improve the performance results of WD trainees. The decision support system is designed and implemented a distributed architecture for performance-oriented management of software agents. The approach provides new agent interaction protocols and utilizes agent performance monitoring and remote synchronization mechanisms. This multi-agent architecture enables direct and indirect agent communication as well as dynamic hierarchical agent coordination. Inter-agent communications use predefined interfaces, protocols, and open channels with specified ontology and semantics. Services can be requested and responses with results received over such communication modes. Both traditional (functional) parameters and nonfunctional (e.g. QoS, deadline, etc.) requirements and captured in service requests

Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design

Computer aided systems human engineering: A hypermedia tool

The Computer Aided Systems Human Engineering (CASHE) system, Version 1.0, is a multimedia ergonomics database on CD-ROM for the Apple Macintosh II computer, being developed for use by human system designers, educators, and researchers. It will initially be available on CD-ROM and will allow users to access ergonomics data and models stored electronically as text, graphics, and audio. The CASHE CD-ROM, Version 1.0 will contain the Boff and Lincoln (1988) Engineering Data Compendium, MIL-STD-1472D and a unique, interactive simulation capability, the Perception and Performance Prototyper. Its features also include a specialized data retrieval, scaling, and analysis capability and the state of the art in information retrieval, browsing, and navigation

Adaptation of WASH Services Delivery to Climate Change and Other Sources of Risk and Uncertainty

This report urges WASH sector practitioners to take more seriously the threat of climate change and the consequences it could have on their work. By considering climate change within a risk and uncertainty framework, the field can use the multitude of approaches laid out here to adequately protect itself against a range of direct and indirect impacts. Eleven methods and tools for this specific type of risk management are described, including practical advice on how to implement them successfully

AI-AUGMENTED DECISION SUPPORT SYSTEMS: APPLICATION IN MARITIME DECISION MAKING UNDER CONDITIONS OF METOC UNCERTAINTY

The ability for a human to overlay information from disparate sensor systems or remote databases into a common operational picture can enhance rapid decision making and implementation in a complex environment. This thesis focuses on operational uncertainty as a function of meteorological and oceanographic (METOC) effects on maritime route planning. Using an existing decision support system (DSS) with artificial intelligence (AI) algorithms developed by New Jersey Institute of Technology and University of Connecticut, cognitive load and time to decision were assessed for users of an AI-augmented DSS, accounting for METOC conditions and their effects, and users of a baseline, 'as is,' DSS system. Scenario uncertainty for the user was presented in the relative number of Pareto-optimal routes from two locations. Key results were (a) users of an AI-augmented DSS with a simplified interface completed assigned tasks in significantly less time than users of an information-dense, complex-interface AI-augmented DSS; (b) users of simplified, AI-augmented DSS arrived at decisions with lower cognitive load than baseline DSS and complex-interface AI-augmented DSS users; and (c) users relied mainly on quantitative data presented in tabular form to make route decisions. The differences found in user performance and cognitive load between levels of AI augmentation and interface complexity serve as a starting point for further exploration into maximizing the potential of human-machine teaming.Office of Naval ResearchMajor, United States Marine CorpsApproved for public release. distribution is unlimite