CANCELLED
ADVANCED INFORMATION TECHNOLOGY
BAA 55-07-03
The Advanced Information Technology (AIT) Branch of the Naval Research Laboratory (NRL) is seeking proposals for innovative research and development in information technology. Current and anticipated areas of research focus include:
(1) Three-dimensional user interface technologies to support information visualization, embedded training and to provide situation awareness. Current work focuses on projective virtual reality and mobile augmented reality. Research topics include: adaptive user interfaces, data representations, multi-user distributed collaboration, system architecture and database designs and the integration of novel hardware and software. In all cases, NRL is particularly interested in the application of human factors and usability-based evaluation methodologies to quantify the benefits of different design choices.
(2) Data information fusion technologies for fusing data from multiple disparate sources in a distributed data fusion network. There are two main thrusts to the research. First, develop generalizable representations of the state of the system through the use of attributes. The numbers of attributes and the relationships between them can be dynamically changed. Uncertainty can be represented in both numerical and analytical forms. Second, develop provably robust data fusion algorithms that can be used to fuse such generalized representations in a dynamic data fusion network. Other research areas may include but are not limited to: scalable architectures; analyzing metadata; aggregation and summarization; pattern recognition across data collections; cross-media context representation; registration, correlation, and linkage; recognizing patterns across multiple domains; predicting and suggesting analyst requests for caching; representation and packaging methods that facilitate communication and preservation of intended knowledge; identifying coverage gaps in the data of interest; cross-cueing in media types and resources; capturing and preserving the processing, transformation, and chain of reasoning along with the raw data used in the analysis; optimizing workflow within a massive data context; and frameworks and theories for the science of integrating massive data, information, knowledge, and people.
(3) Extraction and knowledge discovery technologies to support blue and green force protection and to counter unconventional warfare threats. This research consists of three focus areas. The first focus area is automated information extraction to include the rapid identification and automated extraction of relevant information from message externals (message headers and footers) and message internals (content). The second focus area is automated knowledge discovery to include a) automated identification of groups and group membership; b) automated identification of indicators preceding impending attacks (indicators internal and external to a group); c) automated identification of the common and distinctive components between two or more graph sets, such as those produced by differing analytical tools (link analysis software, etc) or from differing data sets; d) automated pattern learning to discover patterns of interest identified by experts using training data sets. The third focus area involves forecasting threat events incorporating uncertainty. There are three main thrusts within this research area. First, develop and extend current geospatial forecasting techniques incorporating correlations between historical (space and temporal) data. Second, optimize the set of features used for deriving the forecasts. Determining a minimal set of feature layers will reduce the computation time required to generate a forecast. Third, learn the impact that uncertainty, error, and confidence in measurements has on the forecasts. Uncertainty factors in all phases of forecasting including data ingestion, generation of likelihood surfaces, and visualization. In all focus areas, NRL is particularly interested in answering the following questions: Which models are most appropriate to a risk-benefit assessment of threats which are not precisely defined? What means of communicating (medium, formats, etc.) with indigenous populations (including national defense and security forces) best contribute to identifying threats and/or reducing risks? How will extracted information and discovered knowledge be used in a discrete operational risk management model to support operational planning and execution?
(4) Automated intent and deception detection to support force protection and to counter unconventional warfare threats. The first focus area is the development of real-time assessment models of hostile intent and deception. The second focus area is the development of remote, non-invasive sensor technology (i.e., computer vision, IR, voice) to enable the collection of data required to test the models of hostile intent and deception.
(5) Application of multiagent research and related technologies for enhancing decision support capabilities in the Global Information Grid (GIG). Areas of interest may include, but are not limited to, new techniques in mixed-initiative interactions (e.g., human-agent collaboration) as well as the application of machine learning techniques and other artificial intelligence approaches to enable flexible multi-agent coordination and teamwork in open and dynamic environments. We are also interested in the application of game theory to model agent behaviors and interactions to gain an understanding of asymmetrical warfare environment scenarios. Related areas of interest also include new techniques for building and maintaining ontologies, new approaches for utilizing such ontologies to support subsequent agent reasoning, application of semantic web services to enable agents to intelligently discover services and the application of web service orchestration languages to enable agents to compose services. Additional areas of interest include novel and creative applications of emerging technologies such as web 2.0 for developing “mashups”, specifically for supporting collaboration between civil and military authorities in the area of Stability, Security, Transition and Reconstruction (SSTR) operations, humanitarian assistance and disaster relief operations. Other areas of interest include new techniques and approaches (particularly those that are agent-based) to improve situational awareness, particularly for the maritime domain.
(6) Distributed simulation technology. The emphasis is on advanced Modeling and Simulation (M&S) architectures, particularly for distributed systems. The latter includes both classical cluster and shared memory architectures, as well as geographically distributed large-scale training simulations. Areas of current interest include the formal description of math and physics-based models for building composable systems, natural environmental effects servers for M&S architectures, and web-based DoD technology.
Address Initial Proposals to Code 5580, or e-mail, telephone (202) 404-7346. Allow one month before requesting confirmation of receipt of Initial Proposal, if confirmation is desired. Substantive contact should not take place prior to evaluation of an Initial Proposal by NRL. If necessary, NRL will initiate substantive contact.