Engineering and Complex Systems
Information and Networks
Chemistry and Biological Sciences
Information and Networks
The Information and Networks Team within the Engineering and Information Science Branch is organized to support many U.S. Air Force priority areas including autonomy, space situational awareness, and cyber security. The research programs within this team lead the discovery and development of foundational issues in mathematical, information and network oriented sciences. They are organized along three themes: Information, Decision Making, and Networks. The information theme addresses the critical challenges faced by the U.S. Air Force which lie at the intersection of the ability to collect, mathematically analyze, and disseminate large quantities of information in a time critical fashion with assurances of operation and security. Closely aligned with the mathematical analysis of information is the need for autonomous decision making. Research in this theme focuses on the discovery of mathematical laws, foundational scientific principles, and new, reliable and robust algorithms, which underlie intelligent, mixed human-machine decision-making to achieve accurate real-time projection of expertise and knowledge into and out of the battle space. Information analysis and decision making rarely occur in the context of a single source. The networks theme addresses critical issues involving how the organization and interaction among large collections of information providers and consumers contributes to an understanding of the dynamics of complex information systems.
The RTA2 research portfolios and their program officers are listed here:
Complex Network Systems
Program Description: Network behavior is influenced at many levels by fundamental theories of information exchange in the network protocols and policies developed. The Complex Networks program seeks to understand mathematically how such fundamental approaches to information exchange influence overall network performance and behavior. From this analysis we wish to develop strategies to assess and influence the predictability and performance of heterogeneous types of U.S. Air Force networks that must provide reliable transfer of data in dynamic, hostile and high interference environments. Accordingly, we wish to develop approaches to describe information content, protocol, policy, structure, and dynamic behavior of a network by mathematically connecting observed network data to analytic and geometric representation. We can then exploit such mathematical tools in the formulation of network design and engineering approaches in areas such as information and communication theory, signal processing, optimization, and control theory. Examples of such tools might include methods derived from algebraic geometry, algebraic statistics, spectral graph theory, sparse approximation theory, random matrix theory, algebraic graph theory, random field theory, nonparametric estimation theory, algebraic topology, differential geometry, and dynamical systems theory, and quantum information theory. Advances in these mathematical methods will then enable specific ways to model, characterize, design, and manage U.S. Air Force networks and capture and predict the performance of these networks under many diverse conditions.
Basic Research Objectives: Thus methods of consideration in network modeling might include characterizing overall network performance by finding geometric descriptions of embedded parameters of network performance, specific analytic expressions for network behavior derived from inverse methods on network data, and divergence analysis of parameters characterizing one state of a network from another. Characterization of network behavior might include methods classify network behavior and structure through multi-scale vector space and convexity analysis, inference and estimation of networks through algebraic, graph theoretic, and Markov random field descriptions, and understanding of the robustness of given norms and metrics in representing network behavior. Design of networks might involve understanding the efficiency, scaling behavior, and robustness of methods of information exchange including those that use both self and mutual information paradigms. Management of networks may involve assessment of stability and convergence of network protocol and policy for various network dynamical conditions with such properties as curvature, homology class, or geometric flow. Approaches should have specific applicability to U.S. Air Force networking, communications, and architectural design problems but may be drawn from techniques in network analysis from a broad set of disciplines including quantum information systems, materials science and statistical mechanics, molecular and systems biology, wave propagation physics, decision, economics, and game theory to name just a few. From this we can conceive of new directions toward a science of networked systems.
Dr. James H Lawton (703) 696-5999
DSN 426-5999; FAX (703) 696-7360
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Computational Cognition and Machine Intelligence
Program Description: This program supports innovative basic research on the fundamental principles and methodologies needed to enable intelligent machine behavior in support of autonomous and mixed-initiative (i.e., human-agent teaming) systems. The overall vision of this program is that future computational systems will achieve high levels of performance, adaptation, flexibility, self-repair, and other forms of intelligent behavior in the complex, uncertain, adversarial, and highly dynamic environments faced by the U.S. Air Force. This program covers the full spectrum of computational and machine intelligence, from cognitively plausible reasoning processes that are responsible for human performance in complex problem-solving and decision-making tasks, to non-cognitive computational models of intelligence necessary to create robust intelligent autonomous systems. In the midst of this spectrum are the technologies needed to seamlessly incorporate intelligent computational systems into mixed human-agent teams. The program is divided into three sub-areas that span the full spectrum of computational and machine intelligence. They are: Computational Cognition, Robust Decision Making and Machine Intelligence.
The Computational Cognition sub-area supports innovative basic research on high-order cognitive processes that are responsible for good human performance in complex problem solving and decision making tasks - we only want to model the things people excel at. The sub-area also seeks to support research on building computational systems that derive from and/or integrate cognitive and biological models of human and animal intelligence. The overall objective is to understand and exploit these processes to create computational models that perform as well as or better than the reasoning systems they emulate. We are especially interested in the development and evaluation of formal cognitive models that provide an integrative and cumulative account of scientific progress, are truly predictive (as opposed to postdictive), and finally, are generalizable beyond controlled laboratory tasks to information-rich and dynamic real-world tasks.
The Robust Decision Making sub-area is concerned with the need for mixed human-machine decision making, which appears at all levels of U.S. Air Force operations and pervades every stage of U.S. Air Force missions. To that end, new theoretical and empirical guidance is needed to prescribe maximally effective mixtures of human and machine decision making in environments that are becoming increasingly complex and demanding as a result of the high uncertainty, complexity, time urgency, and rapidly changing nature of military missions. Basic research is needed to produce cognitive systems that are capable of communicating with humans in a natural manner, that build trust, are proficient at condensing intensive streams of sensory data into useful conceptual information in an efficient, real-time manner, and are competent at making rapid, adaptive, and robust prescriptions for prediction, inference, decision, and planning. New computational and mathematical principles of cognition are needed to form a symbiosis between human and machine systems, which coordinates and allocates responsibility between these entities in a collaborative manner, achieving comprehensive situation awareness and anticipatory command and control.
The Machine Intelligence sub-area supports innovative basic research on fundamental principles and methodologies of computational intelligence necessary to create robust intelligent autonomous systems. These methodologies are likely to be non-cognitive, taking full advantage of the strengths embodied in mathematical and computational systems, such as the ability of quickly manage vast amounts of data. Robustness in this context is the ability to achieve high performance given at least some or all of the following factors: uncertainty, incompleteness or errors in knowledge; limitations on sensing; real-world complexity and dynamic change; adversarial factors; unexpected events including system faults; and out-of-scope requirements on system behavior.
The program encourages cross-disciplinary teams with cognitive scientists in collaboration with mathematicians, statisticians, computer scientists and engineers, operation and management science researchers, information scientists, econometricians and game theoreticians, etc., especially when the research pertains to common issues and when collaboration is likely to generate bidirectional benefits. This program is aggressive, accepts risk, and seeks to be a pathfinder for U.S. Air Force research in this area. Proposals that may lead to breakthroughs or highly disruptive results are especially encouraged.
Basic Research Objectives: The Computational Cognition sub-area seeks basic research to elucidate core computational approaches that pertain to understanding of the mind and brain (human or animal), as well as cognitively plausible mechanisms inspired by human (or animal) reasoning. In relating formal models to human cognition and performance, research projects should not only ascertain their descriptive validity but also their predictive validity. To this end, the program welcomes work that (1) creates cognitively plausible computational frameworks that (semi-)autonomously integrates model development, evaluation, selection, and revision; and (2) bridges the gap between the fields of cognitive modeling and artificial general intelligence by simultaneously emphasizing important improvements to functionality and also explanatory evaluation against specific empirical results. The program also encourages the development and application of novel and innovative mathematical and neurocomputational approaches to tackle the fundamental mechanisms of the brain, that is, how cognitive behavior emerges from the complex interactions of individual neurobiological systems and neuronal circuits.
The Robust Decision Making sub-area seeks basic research in the areas of (1) Data collection, processing, and exploitation technologies, including mechanisms to help focus attention, analyze and summarize an overload of information, and support inferences in tasks like recognition and identification; (2) Command and Control (C2) technologies, where there is a need for predicting adversarial behavior, sorting the mixture of human and machine responsibilities, and robust planning and scheduling in dynamic environments; and (3) Situation Awareness technologies, where there is a need for a human-system interface that understands human limitations, prioritizes information presentation, and helps achieves symbiosis between human and machine systems in delegating and coordinating responsibilities. In sum, new empirical and theoretical research is needed that provides a deeper understanding of the cognitive requirements of a decision maker with enhanced capability for situation awareness, allows for greater degree of uncertainty in terms of reasoning systems, produces greater robustness and adaptability in planning algorithms in dealing with unexpected interruptions and rapidly changing objectives, generates greater flexibility in terms of assumptions about adversarial agents, and gives clearer guidance for dealing with the complexities encountered in network-centric decision tasks.
The Machine Intelligence sub-area encourages research on building computational systems that enable intelligent behavior based on less-strict cognitive or purely mathematical approaches. The investigative methodology may be theoretical, computational, or experimental, or a combination of thereof. Proposals that lead to advances in the basic principles of machine intelligence for memory, reasoning, learning, action, and communication are desired insofar as these contribute directly towards robustness as defined above. Research proposals on computational reasoning methodologies of any type and combination, including algorithmic, heuristic, or evolutionary, are encouraged as long as the proof of success is the ability to act autonomously or in concert with human teammates to achieve robustness as defined above. Computational intelligence systems often act as human intelligence amplifiers in such areas as planning, sensing, situation assessment and projection; will monitor, diagnose, and control aircraft or spacecraft; and will directly interact with humans and the physical world through robotic devices. Therefore, research that that enables mixed-initiative interaction and teaming between autonomous systems and human individuals or teams is an important part of the program.
Dr. James H Lawton (703) 696-5999
DSN 426-5999; FAX (703) 696-7360
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Program Description: This program seeks to develop innovative mathematical methods and fast, reliable algorithms aimed at making radical advances in computational science. Research in computational mathematics underpins foundational understanding of complex physical phenomena and leads to capabilities for analysis and prediction of phenomena crucial to design and control of future U.S. Air Force systems and processes. Proposals to this program should focus on fundamental scientific and mathematical innovations. Additionally, it is desirable to frame basic research ideas in the context of applications of relevance to the U.S. Air Force which can serve simultaneously to focus the research and to provide avenues for transition of basic research outcomes into practice. Applications of current interest include, but are not limited to, unsteady aerodynamics, plasma dynamics, propulsion, combustion, directed energy, information science, and material science.
Basic Research Objectives: Research under this program has traditionally emphasized schemes that address the discretization and numerical solution of complex systems of equations, generally partial differential equations that arise from physics. Nevertheless, alternative phenomenological models and computational approaches are of interest, particularly in connection with emerging applications involving information and biological sciences.
To meet the formidable computational challenges entailed in simulating nonlinear, discontinuous, multi-physics and multi-scale problems of interest to the U.S. Air Force, the program is examining numerical algorithms that include multi-scale and multi-physics approaches with particular emphasis on convergence, error analysis, and adaptivity. A spectrum of numerical methods in these areas are being developed and improved within the scope of the program, including high-order spatial and temporal algorithms, mesh-free and particle methods, high-order moving interface algorithms, and hybrid methods. The other areas of interest are rigorous model reduction techniques with quantifiable fidelity for efficient and robust multidisciplinary design and optimization, scalable algorithms for multi-core platforms and also uncertainty quantification (UQ). The active areas of interest in UQ include development of high accuracy stochastic numerical methods, stochastic model reduction and long term time integration techniques. Given the emerging computing platforms, including multicore-based platforms with complex architectures, the program is considering fundamental research on the mathematical aspects of scalable solvers with emphasis on parallelism across scales, high-order discretization, and multi-level domain decomposition techniques.
Dr. Jean-Luc Cambier (703) 696-1141
DSN 426-1141; FAX (703) 696-7360
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Dynamics and Control
Program Description: This program emphasizes the interplay of dynamical systems and control theories with the aim of developing innovative synergistic strategies for the design and analysis of controlled systems that enable radically enhanced capabilities, including performance and operational efficiency for future U.S. Air Force systems. Proposals should focus on the fundamental science and mathematics, but should include connectivity to appropriate Air Force applications. These applications currently include information systems, as well as autonomous/semi-autonomous aerial vehicles, munitions, and space vehicles.
The dramatic increase in complexity of Air Force systems provides unique challenges for the Dynamics and Control Program. Meeting these challenges may require interdisciplinary approaches as well as deeper studies within single disciplines. Lastly, note that the Dynamics and Control Program places special emphasis on techniques addressing realistic treatment of applications, complexity management, semi-autonomous systems, and real-time operation in stochastic and adversarial environments.
Basic Research Objectives: Current research interests include: adaptive control and decision making for coordinated autonomous/semi-autonomous aerospace vehicles in uncertain, information rich, dynamically changing, networked environments; understanding how to optimally include humans in the design space; novel schemes that enable challenging multi-agent aerospace tracking in complex, cluttered scenarios; robust and adaptive non-equilibrium control of nonlinear processes where the primary objective is enhanced operability rather than just local stability; new methods for understanding and mitigating the effects of uncertainties in dynamical processes; novel hybrid control systems that can intelligently manage actuator, sensor, and processor communications in a complex, spatially distributed and evolving system of systems; sensor rich, data driven adaptive control; and applying control concepts motivated by studies of biological systems. In general, interest in the control of large complex, multi-scale, hybrid, highly uncertain nonlinear systems is increasing. Further, new mathematics in clear support of dynamics and control is of fundamental importance. In this regard, some areas of interest include, but are not limited to, stochastic and adversarial systems, partial and corrupted information, max-plus and idempotent methods, game theory, nonlinear control and estimation, and novel computational techniques specifically aimed at games, control and systems theory.
Dr. Frederick A. Leve (703) 696-7305
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Dynamic Data Driven Applications Systems (DDDAS)
Program Description: The DDDAS concept entails the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement (instrumentation and control) components of the application system. DDDAS is a key concept for improving modeling of systems under dynamic conditions, more effective management of instrumentation systems, and is a key concept in architecting and controlling dynamic and heterogeneous resources, including, sensor networks, networks of embedded controllers, and other networked resources. DDDAS transformative advances in computational modeling of applications and in instrumentation and control systems (and in particular those that represent dynamic systems) require multidisciplinary research, and specifically need synergistic and systematic collaborations between applications domain researchers with researchers in mathematics and statistics, researchers computer sciences, and researchers involved in the design/ implementation of measurement and control systems (instruments, and instrumentation methods, and other sensors and embedded controllers).
Basic Research Objectives: Individual and multidisciplinary research, technology development, and cyberInfrastructure software frameworks needed for DDDAS applications and their environments are sought, along four key science and technology frontiers: Applications modeling: In DDDAS an application/simulation must be able to accept data at execution time and be dynamically steered by such dynamic data inputs. This requires research advances in application models that: describe the application system at different levels of detail and modalities; are able to dynamically invoke appropriate models as needed by the dynamically injected data into the application; and include interfaces of applications to measurements and other data systems. DDDAS will, for example, engender an integration of large scale simulation with traditional controls systems methods, thus provide an impetus of new directions to traditional controls methods. Advances in Mathematical and Statistical Algorithms include creating algorithms with stable and robust convergence properties under perturbations induced by dynamic data inputs: algorithmic stability under dynamic data injection/streaming; algorithmic tolerance to data perturbations; multiple scales and model reduction; enhanced asynchronous algorithms with stable convergence properties; multimodal, multiscale modeling and uncertainty quantification, and in cases where the multiple scales or modalities are invoked dynamically and there is need for fast methods of uncertainty quantification and uncertainty propagation across dynamically invoked models. Such aspects push to new levels of challenges the traditional computational math approaches. Application Measurement Systems and Methods include improvements and innovations in instrumentation platforms, and improvements in the means and methods for collecting data, focusing in a region of relevant measurements, controlling sampling rates, multiplexing, multisource information fusion, and determining the architecture of heterogeneous and distributed sensor networks and/or networks of embedded controllers. The advances here will create new instrumentation and control capabilities. Advances in Systems Software runtime support and infrastructures to support the execution of applications whose computational systems resource requirements are dynamically dependent on dynamic data inputs, and include: dynamic selection at runtime of application components embodying algorithms suitable for the kinds of solution approaches depending on the streamed data, and depending on the underlying resources, dynamic workflow driven systems, coupling domain specific workflow for interoperation with computational software, general execution workflow, software engineering techniques. The systems software environments required are those that can support execution in dynamically integrated platforms ranging from the high-end to the real-time data acquisition and control - cross-systems integrated. Software Infrastructures and other systems software (OS, data-management systems and other middleware) services to address the "real time" coupling of data and computations across a wide area heterogeneous dynamic resources and associated adaptations while ensuring application correctness and consistency, and satisfying time and policy constraints. Specific features include the ability to process large volume, high rate data from different sources including sensor systems, archives, other computations, instruments, etc.; interfaces to physical devices (including sensor systems and actuators), and dynamic data management requirements.
Areas of interest to the AF and which can benefit from DDDAS transformative advances, include areas driven by the AF Technology Horizons, Energy Horizons, and Global Horizons reports, such as: autonomous systems (e.g. swarms of unmanned or remotely piloted vehicles); autonomous mission planning; complex adaptive systems with resilient autonomy; collaborative/cooperative control; autonomous reasoning and learning; sensor-based processing; ad-hoc, agile networks; multi-scale simulation technologies and coupled multi-physics simulations; decision support systems with the accuracy of full scale models (e.g. high-performance aircraft health monitoring, materials stresses and degradation); embedded diagnostics and V&V for complex adaptive systems; automated software generation; cognitive modeling; cognitive performance augmentation; human-machine interfaces. DDDAS provides new approaches for combining computational, theoretical, and instrumentation data sets for high interactive testing of multiple physical and engineering hypotheses.
Programmatic activities that will be launched under this initiative will support research in individual areas, but mostly in the context of multidisciplinary research across at least two of the four components under Basic Area Objectives above.
Dr. Frederica Darema (703) 588-1926
DSN 426-7551; FAX (703) 696-8450
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Information Operations and Cybersecurity
Program Description: Securing cyberspace, defending against and preventing cyber attacks are not new but have become increasingly pressing in the light of technological advancements. Software and protocols are only becoming more complex to meet application demands. More flexible computing environments such as distributed systems need new ways of thinking to ensure secure end-to-end functionalities even though components are only known to be individually secure. The emergence of nano-scale and quantum computing with various physical realizations and different architectures further compound technological challenges for cybersecurity.
Although engineering practices continue to provide short-term and temporary relieves to these pressing needs, new scientific ideas are required to address the inherent insecurity in cyberspace. Many fundamental concepts are still eluding precise formulation and awaiting rigorous responses. The goal of this Basic Research program is to explore novel, promising ideas and methodologies that can establish a firm scientific foundation for cybersecurity and potentially tackle the difficult technical hurdles put forth in the previous paragraph.
Basic Research Objectives: Mathematical formalisms and logical constructs will likely continue to play a central role in the development of a Science of Cybersecurity. Recent developments and advances in formal semantics, type theory, modern cryptography, interactive and automated theorem proving, logics, programming languages, constructive mathematics, game theory, etc. are expected to provide valuable insights into the modeling, expressing, analyzing, verifying, and reasoning tasks of security policies or goals in terms of their formal properties.
Research areas of interest include, but are not limited to: composition of security properties and protocols in view of distributed interactive systems; primitives for various security mechanisms that can support compositionality; abstract formulations that can unify key security properties of hardware and software aspects; information flow security and noninterference in dynamic and distributed settings; rigorous techniques to enable persistent and secure operations on unsecure systems; new formulations of invariants associated with security properties that can readily be computed and interpreted; rigorous formulation and construction of obfuscation techniques for software programs or hardware architecture designs to enhance security. Many of these research topics are not restricted only to digital technologies.
Dr. Tristan Nguyen (703) 696-7796
DSN 426-7796 FAX (703) 696-7360
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Optimization and Discrete Mathematics
Program Description: The program goal is the development of mathematical methods for the optimization of large and complex models that will address future decision problems of interest to the U.S. Air Force. Areas of fundamental interest include resource allocation, planning, logistics, engineering design and scheduling. Increasingly, the decision models will address problems that arise in the design, management and defense of complex networks, in robust decision making, in performance, operational efficiency, and optimal control of dynamical systems, and in artificial intelligence and information technology applications.
Basic Research Objectives: There will be a focus on the development of new nonlinear, integer and combinatorial optimization algorithms, including those with stochastic components. Techniques designed to handle data that are uncertain, evolving, incomplete, conflicting, or overlapping are particularly important.
As basic research aimed at having the broadest possible impact, the development of new computational methods will include an emphasis on theoretical underpinnings, on rigorous convergence analysis, and on establishing provable bounds for (meta-) heuristics and other approximation methods.
Dr. Jean-Luc Cambier (703) 696-1141
DSN 426-1141; FAX (703) 696-7360
Email: ODMath@us.af.mil or email@example.com
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Science of Information, Computation and Fusion
Program Description: The U.S. Air Force collects vast amounts of data through various modes at various times in order to extract and derive needed "information" from these large and heterogeneous (mixed types) data sets. Some data, such as those collected from magnetometers, register limited information content which is more identifiable at the sensor level but beyond human's sensory reception. Other types of data, such as video cameras or text reports, possess more semantic information that is closer to human's cognition and understanding. Nevertheless, these are instances of disparate data which encapsulate different types of "information" pertained to, perhaps, the same event(s) captured by different modalities through sensing and collection.
In order to understand and interpret information contained in various data sources, it is necessary to extract relevant pieces of information from these datasets and to make inferences based on prior knowledge. The discovery of relevant pieces of information is primarily a data-driven process that is correlational in nature and, hence, offers point solutions. This bottom-up processing direction needs conceptually-driven reasoning to integrate or fuse the previously extracted snippets of information by leveraging domain knowledge. Furthermore, the top-down process can offer causal explanation or causal inference, generate new hypotheses, verify or test hypotheses in light of observed datasets. Between the data-driven and conceptually-driven ends, there may reside different levels of abstraction in which information is partially extracted and aggregated based on the nature of applications.
Basic Research Objectives: With the rationale and guiding principles outlined in the above paragraph, this program seeks fundamental research that potentially leads to scientific advancements in informatics and computation which can support processing and making sense of disparate information sources. After all, information processing can formally and fundamentally be described as computing and reasoning on various data structures. Successes in addressing the research sub-areas stated below would give the U.S. Air Force new capabilities to: (1) shift emphasis from sensing to information; (2) understand the underpinning of autonomy; (3) relieve human's cognitive overload in dealing with the data deluge problem; (4) enhance human-machine interface in information processing.
To accomplish the research objectives, this program focuses on, but is not limited to, new techniques in mathematics, computer science, statistics and logic which have potentials to: (1) cope with various disparate and complex data types; (2) construct expressive data structures for reasoning and computation; (3) bridge correlational with causal discovery; (4) determine solutions or obstructions to the local-to-global data-fusion problem; (5) mechanize reasoning and computing in the same computational environment; (6) yield provably efficient procedures to enable or facilitate data analytics; (7) deal with high-dimensional and massive datasets with provably guaranteed performance.
Dr. Doug Riecken (703) 696-9736
DSN 426-9736; FAX (703) 696-7360
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Sensing, Surveillance and Navigation
Program Description: This research activity is concerned with the systematic analysis and interpretation of variable quantities that represent critical working knowledge and understanding of the changing battle-space. Sensor/Surveillance images are of special importance in targeting, damage assessment and resource location. Signals are either naturally or deliberately transmitted, propagated as electromagnetic waves, and recaptured at the receiving sensor. Modern radar, infrared, and electro-optical sensing systems produce large quantities of raw signals that exhibit hidden correlations, are distorted by noise, but still retain features tied to their particular physical origin. Statistical research that treats spatial and temporal dependencies in such data is necessary to exploit its usable information.
Basic Research Objectives: An outstanding need in the treatment of signals is to develop resilient algorithms for data representation in fewer bits (compression), image reconstruction/enhancement, and spectral/frequency estimation in the presence of external corrupting factors. These factors can involve deliberate interference, noise, ground clutter, and multi-path effects. This AFOSR program searches for application of sophisticated mathematical methods, including time-frequency analysis and generalizations of the Fourier and wavelet transforms, that deal effectively with the degradation of signaling transmission across a channel. These methods hold promise in the detection and recognition of characteristic transient features, the synthesis of hard-to-intercept communications links, and the achievement of faithful compression and fast reconstruction for video and multi-spectral data. New combinations of known methods of asset location and navigation are being sought, based on analysis and high-performance computation that bring a force-multiplier effect to command/control capabilities. Continued upgrade and reliance on Global Positioning System makes it critical to achieve GPS-quality positioning in situations where GPS by itself is not sufficient. Ongoing research in Inertial and non-Inertial navigation methods (including optical flow and use of signals of opportunity) will bring location precision and reliability to a superlative level. Continuous improvement in its repertoire of signal processing and statistical tools will enable the U.S. Air Force to maintain its lead in battle-space awareness through navigation and surveillance. Communications are what hold together the networked infosphere and cost-effective systems innovations that enable phenomenal air power projection.
Dr. Arje Nachman (703) 696-8427
DSN 426-8427; FAX (703) 696-8450
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Systems and Software
Program Description: The AF's mission is to "fly, fight, and win in air, space, and cyberspace." In order to accomplish its mission, the AF invests in Systems & Software, which is the keystone of all advanced technology. The Systems & Software program actively searches for ideas with respect to two submissions: 1) Improving current AF systems, and; 2) Introducing cutting-edge research to expand the field of knowledge. Improving current AF systems is needed; the AF's use of legacy systems is well known, along with the detrimental issues of legacy system use. There are many AF systems which have extremely long life cycles (such as combat system software). In order to ensure that these legacy systems are up-to-date, new systems infrastructures are investigated. Additionally, new areas of Systems & Software are encouraged to ensure that the AF continues to be on the cutting-edge of technology; novel areas include entirely new directions that will have significant impact in the future. Overall examples of areas include operating systems, compilers, virtual memory, multi-core platforms, etc. AFOSR is looking for research that will drastically improve current AF systems and help to develop new S&T for the benefit of the nation.
Basic Research Objectives: As stated above, Systems & Software addresses two issues - both the new and the old: 1) New Technology Research (such as, but not limiting to, multi-core and many-core systems), and; 2) Legacy System Research (concerning existing AF systems such as, but not limiting to, operating systems, software, etc.). Since Systems & Software direction is continually changing, i.e., technology life-span of approximately 3 years or less, specific subareas are not specifically stated within this BAA; instead, due to the topical nature of the field, the specific area of research is open to the proposer, as long as the research addresses either issue - New Technology Research or Legacy System Research - in Systems & Software. Any new ideas of either of the two issues are welcomed.
Researchers are highly encouraged to contact the Program Officer prior to developing full proposals to briefly discuss the current state-of-the-art, how the proposed effort would advance it, and the approximate yearly cost for a three to five year effort.
Dr. Kathleen M. Kaplan (703) 696-7312
DSN 426-7312; FAX (703) 696-7360
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Trust and Influence
Program Description: The Trust and Influence program is motivated by recent technological advances in the area of unmanned and autonomous systems, and the strategic environment that the U.S. Air Force is expected to face in the future; a significant departure from that which has dominated most of its history. The Air Force is facing a broader range of threats that are less predictable, with many conflicts occurring in failed or failing states that include radical extremists and a wide range of non-state actors. Moreover, the rapid advances and proliferation of advanced autonomous systems are expected to fundamentally change the way the Air Force operates. To address these challenges, the Trust and Influence program invests in the development of the theoretical and empirical foundations of reliance and contemporary influence. Specifically, we are concerned with investigating the mechanisms by which humans establish, maintain, and repair trust in other agents, both human and machine. The science of influence or persuasion will expand our understanding for how we might shape the behaviors, attitudes and beliefs of others. The resulting portfolio directly enhances the Air Force's technology development programs, and will impact policies and operations related to national security. Trust and Influence invests in the discovery of the foundational concepts of effective influence, deterrence, trust-building, trust calibration, and counter-terrorism operations. Multi-disciplinary approaches are encouraged, to include cognitive science, neuroscience, anthropology, sociology, linguistics, economics, computer science and mathematics. Research designs that incorporate laboratory studies, modeling or field research leading to transformative novel theories are also encouraged.
Basic Research Objectives: The basic research interests under this program can be defined broadly by three areas: trust in autonomous systems, cross-cultural trust, and socio-digital influence. In the area of trust in autonomous systems there is particular interest in (1) empirical studies to examine drivers of trust between humans and intelligent, autonomous or robotic agents, (2) laboratory and field studies to examine the impact of socially-designed cues or physical features such as appearance, voice, personality, and other social elements on human trust and system performance, (3) development of trust metrics and other relevant constructs in human-machine teaming with a particular focus on real-time and dynamic assessment, and (4) modeling of human-machine teaming that supports adaptive and continuous improvement of joint performance in complex environments. In the area of cross-cultural trust, there is interest in (1) developing theories of interpersonal and organization trust that account for various cultural constructs and characteristics, (2) revealing the antecedents of trust in cross-cultural interactions, and (3) cultural differences in complex human-machine interaction. In the area of socio-digital influence there is a need for (1) laboratory and field studies to reveal sources of influence and persuasion in social media and across different cultural groups, (2) social, cognitive, and neural mechanisms of influence and persuasion (3) modeling and measuring the relationship between online and real-world behaviors, (4) empirical studies to discover new theories of influence as it pertains to the cyber domain, and (5)understanding the behavioral effects of influence tactics such as foreign policies or developmental activities.
Researchers are encouraged to contact the Program Officer prior to developing full proposals to discuss alignment of the researchers' ideas with program goals, their proposed methods and scope of the proposed effort.
Dr. Benjamin A Knott (703) 696-1142
DSN 426-1142; FAX (703) 696-7360
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