National Science Foundation Awards Knowledge And Distributed Intelligence GrantsSeptember 29, 1998
The National Science Foundation (NSF) is awarding a series of 40 new grants worth more than $51.5 million in cross-cutting research through its agency-wide Knowledge and Distributed Intelligence (KDI) initiative. Nearly 50 institutions will be part of this very broad scientific enterprise that could lead to rapid and radical interdisciplinary advances in: Knowledge Networking (KN) (e.g., employing a distributed cognition approach to designing digital work materials for collaborative workplaces); Learning and Intelligent Systems (LIS) (e.g., designing intelligent software agents to control and optimize resource allocation in large-scale computer networks); and New Computational Challenges (NCC) (e.g., modeling defects in solid materials at multiple levels).
"NSF's cultivation of this highly multidisciplinary research arena," said NSF Director Rita Colwell, "will change the way scientists collaborate and the way they prepare to examine the world as they seek new frontiers for discovery."
The explosive growth in computer power and connectivity is reshaping relationships among people and organizations while also transforming the processes of discovery, learning and communication. Similar growth in scientific understanding of learning and intelligence in natural systems and artificial systems is contributing to unprecedented research opportunities in these areas.
"This investment will be making our high-speed, high-volume information systems more human-centered, more 'intelligent'_a place where people and machines collaborate beyond their physical presence," said Joseph Bordogna, NSF's acting deputy director. "We are entering an era in which insight into complex problems will be more readily garnered." This is an age of global intellectual and commercial environments "in which knowledge will be available to anyone, located anywhere, at any time." KDI will help keep the "information age" from becoming the "information overload age," said Bordogna.
Through KDI, NSF aims to achieve the next generation of human capability to generate, model and represent more complex and cross-disciplinary scientific data from new sources and at enormously varying scales; to transform this information into knowledge by combining and analyzing it in new ways; to deepen our understanding of learning and intelligence in natural and artificial systems; to explore the cognitive, ethical, educational, legal and social implications of new types of learning, knowledge and interactivity; and to help scientists collaborate in sharing knowledge and working together.
These 40 new grants represent the full complement of research themes (KN, LIS, and NCC) within the Foundation-wide investment in KDI. Nearly 700 research proposals were received and reviewed by an advisory panel of 235 experts representing the full spectrum of scientific disciplines within the purview of the KDI initiative.
"We were impressed with the number of excellent proposals," said Michael McCloskey, NSF's KDI coordinator. "I wish we had been able to fund more of them."
In addition to NSF's Office of Polar Programs, all six research directorates are collaborating on the KDI grants, including Biological Sciences; Computer and Information Science and Engineering; Education and Human Resources; Engineering; Mathematical and Physical Sciences; and Social, Behavioral and Economic Sciences.
Attachment:Examples of KDI awards
KNOWLEDGE AND DISTRIBUTED INTELLIGENCE AWARDS (1998)Below are examples of KDI awards. Included are title, award number, principal investigators and institution. More information can be accessed (via the award number) at: http://www.nsf.gov/verity/srchawdf.htm More details about KDI are available at: www.nsf.gov/kdi. (KN=Knowledge Networks; LIS=Learning and Intelligent Systems; NCC=New Computational Challenges.)
- Multiscale Physics-Based Simulation of Fluid Flow for Energy and Environmental Applications (NCC)
Award Number: 9873326
Investigator: Mary F. Wheeler, Steven L. Bryant, Todd J. Arbogast, Clinton N. Dawson, Chandrajit L. Bajaj
University of Texas-Austin-Austin, TX
This project models the movement and interaction of fluids in surface waters and subsurface groundwater and petroleum reservoirs. Applications include petroleum and natural gas production; groundwater contamination and remediation; surface water circulation and pollution and the interaction between surface and groundwater environments. These applications have potential significant environmental and economic impact on the availability of fossil fuels and clean water for drinking and manufacturing. Underground reservoirs may be miles long, while their oil or water percolates through microscopic pores in the rock. Modeling the flow requires enormous range of scales: from a fraction of an inch up to miles. Moreover, different physical processes that affect the flow occur simultaneously in different parts of the reservoir. The investigators will study how small-scale processes affect larger, field-scale processes, and how different physical processes occurring in close proximity affect each other. The investigators will develop new mathematical models, numerical algorithms, computational science and visualization tools, laboratory experiments, and techniques and strategies for viewing results interactively and with collaborators off site.
- Sequential Decision Making in Animals and Machines (LIS)
Award Number: 9873531
Investigators: John M. Henderson, Sridhar Mahadevan, Fred C. Dyer
Michigan State University-East Lansing, MI Mobile organisms make accurate behavioral decisions with extraordinary speed and flexibility in real-world environments despite incomplete knowledge about the state of the world and the effects of their actions. This ability must be shared by artificial agents, such as mobile robots, if they are to operate flexibly in similar environments. The main goal of the research is to undertake a detailed interdisciplinary study of sequential decision making across animals and robots, with a focus on real-time learning and control of information gathering and navigational behaviors. The project will take a comparative approach, combining psychophysical and cognitive research techniques from the study of human eye-movement control, behavioral research techniques from the study of insect navigation, and computational methods from the study of mobile robots. All of these systems provide experimentally tractable test-beds for studying real-time decision making in partially observable environments.
- Multiscale Modeling of Defects in Solids (NCC)
Award Number: 9873214
Investigators: James P. Sethna, Christopher R. Myers, Paul R. Dawson, Anthony R. Ingraffea
Cornell University-Ithaca, NY The deformation and failure of solids, such as metals and metallic alloys used widely in engineering, involve the formation and evolution of complex defect structures on a hierarchy of length scales. For example, as an airplane wing is buffeted by turbulence during flight, complicated changes happen to the internal structure of the metal which can lead_in the absence of inspection and maintenance_to its failure. The proposed research will examine how: the atoms in the metal form defect lines (called dislocations); the dislocations form into larger tangles and arrays; the tangles form small gaps or voids; the tangles and voids lead to the formation of microscopic cracks; and cracks grow to break the wing. The multidisciplinary research team will use computers to explicitly model defect dynamics at each scale. The team will exploit novel software techniques to link supercomputer simulations with visualization utilities and a suite of analysis tools. The assembly of these tools will form the Digital Material: a virtual laboratory to explore and develop theories and models of defect dynamics, which combine insights from many disciplines, and to test and validate those models over a wide range of length scales. The ability to better understand and model materials will drive economic and technological advances in the next century in fields including heavy industry, transportation, advanced materials design, and microelectronics.
- Artificial Implementation of Cerebro-Cerebellar Control of Reaching and Walking (LIS)
Award Number: 9873478
Investigators: Jean-Jacques E. Slotine, Gill A. Pratt, Munther A. Dahleh, Timothy J. Ebner
Massachusetts Institute of Technology-Cambridge, MA This project is designed to verify and further develop a model of the operation of the cerebellum. The researchers will attempt to correlate nerve signals observed in active experimental primates with those predicted by the model and to account for motor behavior of healthy humans as well as those suffering from cerebellar dysfunction. The model includes the roles of the intermediate and lateral cerebellum and parts of the cerebrum, spinal cord, peripheral nerve and associated muscles. The performance of the model will be analyzed from the perspective of robot balance and leg control. The researchers hope to develop a model of human (primate) cerebellar system function that is physiologically, neuroanatomically and quantitatively accurate, and also fully comprehensible in engineering terms. Anticipated applications include more precise and specific interpretation of functional neuroimaging data, improved rational design of neuroprosthetic devices and neurosurgical interventions and the design of more behaviorally adaptable, well-coordinated and agile robots.
- Automated Learning in Network Traffic Control (LIS)
Award Number: 9873469
Investigators: Bhubaneswar Mishra, Rohit J. Parikh
New York University-New York, NY This is a design and implementation study of automated intelligent agents suitable for controlling and optimizing resource allocation in large-scale networks. This area is characterized by sparsely-connected, computational elements that interact solely through the use of joint resources in massively distributed environments. The mathematical underpinnings of this research program are found in economics. In contrast to classical theory, however, the underlying computational model of strategy selection here involves inductive learning and discovery processes. The here goal is to provide a rigorous treatment of the reasoning behavior of automated agents by using belief-revision logics to model belief-based learning. The long-term significance of the proposed research is the dissemination of automated agents suitable for control and optimization in large-scale, geographically distributed computer networks, without the benefit of common knowledge of the state of the entire system.
- The Effects of Representational Bias on Collaborative Learning Interactions (LIS)
Award Number: 9873516
Investigator: Daniel D. Suthers
University of Hawaii-Manoa-Honolulu, HI At a time when public schools are making larger investments in hardware and software, and colleges and universities are increasingly turning to distance-education technology to reach a broader customer base, it is critical to maximize the effectiveness of technology for learning. This project will improve understanding of how collaborative learning is facilitated by computer software with which learners construct and manipulate visual representations of their emerging knowledge. "Representational bias" refers to how these software environments facilitate the expression and inspection of different kinds of information. The research will provide a better theoretical understanding of the role of representational bias in guiding collaborative learning and problem solving processes. Such an understanding will inform the design of more effective collaborative learning and distance learning environments, and will also have applications to the design of representational tools for a variety of other knowledge networking applications, such as collaborations between scientists and other practitioners.-end-
National Science Foundation
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