Research concentrates on understanding and exploiting interactions between information and intelligent systems, such as software agents or robots. Information can be thought of as data in context. In order to fully exploit that data, the context must be taken into account. The data can then be used in providing automated intelligence: perception, reasoning, planning, collaborating, and decision-making. These broad issues in automated intelligence can be applied to a wide range of systems and environments, like cyber virtual environments or decision support. Aspects of Intelligent Systems complement research conducted in the Sciences for Maneuver Campaign, which focuses on Intelligent Systems concepts applied to vehicles or robotic platforms.
Intelligent Information Management for the Battlefield (ALC)
Tactical edge networks are extremely constrained in terms of their communications links and their computational capabilities. The network links are characterized as Disconnected, Intermittent, and Limited (DIL) and often do not have the capacity to provide all the necessary information to the dismounted soldier. Therefore, we need mechanisms that can discriminate, prioritize, and filter information that is relevant to a soldier so that the available network channel capacity can be maximized. The scientific challenge is grounded in practical machine learning approaches targeting adaptive filtering and prioritization methods that can be used to determine value of information (VoI), in the context of military operations, based on the soldier’s context (mission, location, activity, and situation). The technical problem is to be able to realize these theoretical approaches in the austere, distributed, tactical-edge battlefield environment where access to enterprise-grade communications or compute may not exist. Desired research methodologies include algorithm design, prototype development, laboratory experimentation, and field experimentation.
Niranjan Suri, firstname.lastname@example.org, (301) 394-5626
Social Computing (ALC)
Social computing refers to the analysis of socially created information using computational technologies. The driving force behind social computing is the ability to extract patterns and make predictions using computational methodologies as applied to social information. The research conducted in this area applies computing and information science principles to the solution of problems in application domains that lie outside the scope of the traditional computing discipline. As such, coordination and collaboration with interdisciplinary social scientists is essential to success.
Judith Klavans, Judith.L.Klavans.email@example.com, (301) 394-2368
Joint Text and Video Analytics (ALC)
The planned program will develop methods for integrating (NL) text & video analytics to enhance speed and accuracy of situational awareness (SA). Currently, automatic text analysis and video analysis have been pursued independently even though the two forms co-occur frequently with respect to a given SA, resulting in pervasive inefficiencies and in loss of potentially exploitable data. Our methods will target problems of retrieving images or video by NL query, generating NL descriptions of images or video (referring-expression or summary generation), and jointly exploiting video and text when elements of both are present in the data, such as making use of one medium to help parse the other and making use of both media to construct integrated SA. Toward developing these methods, we will build new annotated, aligned, text and video datasets.
Computational Intelligence (ALC)
Research seeks computationally feasible techniques and theories to address the synergistic integration of components of intelligent behavior, including perception, reasoning, planning/execution, and decision making. These behaviors can be individual or collaborative. This includes: (1) semantic perception and scene understanding investigating innovative concepts for recognizing and understanding the world in which the system’s intelligent agents operate; (2) robot planning and behaviors in complex environments; (3) reasoning under uncertainty;
Stuart Young, firstname.lastname@example.org, (301) 394-5618
Reasoning and Decision Making Under Uncertainty (ALC)
Research in developing a method for analogical and deductive reasoning and decision making by automated systems such as intelligence analysis systems or battlefield robots. In order to deal with the inherent uncertainty that comes with military operations, this approach needs to be able to handle approximate and partial information. Our method includes both a rich common-sense knowledge base for deductive reasoning and a distributional semantic vector space trained on large volumes of text corpora for analogical reasoning at query-time. This combination gives the system the ability to answer questions approximately when full information is not available. Our system can, for example, reason that because a tank is similar to a truck, it probably contains a steering mechanism and a transmission.
Doug Summers-Stay, email@example.com, (301) 394 -1990