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NSF PR 98-61 - September 29, 1998
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NSF Awards Knowledge and Distributed Intelligence
Grants
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.
Editors: More details about KDI and specific
KDI grants are available at:
http://www.nsf.gov/kdi.
Attachment: Examples of
KDI awards
Attachment
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.
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