DARPA Program announcement (long, 1 of 3)

[Scott.Fahlman@B.GP.CS.CMU.EDU]

Barbara Yoon at DARPA has apparently been flooded with requests for the
three DARPA program announcements in the neural network area.  To lighten
the load, she asked us to send out the full text of these announcements to
members of this mailing list.  The text in this and the following two
messages is copied verbatim from the Commerce Business Daily.  We have
resisted the temptation to insert paragraph breaks to improve readability.

I apologize for dumping so much text on people who alrady have copies of
the announcements or who are not interested, but this seems the best way to
get the word out to a large set of potentially interested people.  Please
don't contact us about this program -- the appropriate phone numbers and
addresses are listed in the announcements.

-- Scott Fahlman, CMU

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Defense Advanced Research Projects Agency (DARPA), Contracts Management (CMO),
1400 Wilson Blvd., Arlington, VA 22209-2308

A--BROAD AGENCY ANNOUNCEMENT (BAA#89-02):  NEURAL NETWORKS:  COMPARATIVE
PERFORMANCE MEASUREMENTS SOL BAA#89-02 DUE 030189 POC Douglas M. Pollock, 
Contracts, (202)694-1771; Dr. Barbara L. Yoon, Technical, (202)694-1303.
The Defense Advanced Research Projects Agency, Defense Sciences Office, 
DARPA/DSO is interested in receiving proposals to construct and test software
simulations of artificial neural networks (or software simulations of hybrid
systems incorporating artificial neural networks) that perform defined,
complex classification tasks in the following application areas: (1) Automatic
target recognition; (2) Continuous speech recognition; (3) Sonar signal
discrimination; and (4) Seismic signal discrimination.  The objectives of this
program are to advance the state-of-the-art in application of artificial neural
network approaches to classification problems; to investigate the optimal
role of artificial neural networks in hybrid  classification systems; and
to measure the projected performance of artificial neural networks (or hybrid
systems containing neural networks) in order to support a comparison with
the performance of alternative, competing technologies.  DARPA will provide
application developers with a standard set of training data, appropriate to
the application, to be used as the basis for training (or otherwise developing)
their classification systems.  The systems developed will then be evaluated
independently in classification of standard sets of test data, distinct from
the training set.  The four application tasks are more fully described below.
(1) Automatic target recognition: (a) Given a multi-spectral training set of
time-correlated images of up to ten land vehicles (which may be partially
obscured and in cluttered environments) with ground truth provided, identify
and classify these vehicles in a new set of images (outside the training set);
(b) Given images of two or more new land vehicles, recognize these vehicles
as distinct from the original set and distinguish them from one another (with
no system reprogramming or retraining); (c) Given a new training set of data 
on air vehicles, with system reprogramming and/or retraining, modify the system
to identify and classify this new class of targets.  (2) Continuous speech
recognition:  (a) Given a training set of 2800 spoken English sentences (with
a 1000 word vocabulary), transcribe to written text spoken English sentences
from a test set (outside the training set); (b) With no system reprogramming
or retraining, transcribe to text spoken English sentences using vocabulary
outside the initial vocabulary (given only the phonetic spelling of the new
words); (c) Given training data on spoken foreign language sentences (with
characteristics similar to those of the English sentence data base described
in application (2)(a) above), with system programming and/or retraining, modify
the system to transcribe to text spoken foreign language sentences.  (3) Sonar
signal discrimination:  (a) Given a training set of several acoustic signature
transients and passive marine acoustic signals (both signal types in noisy
environments), detect and classify each signal type in a test set (outside
the training set); (b) Given two or more new passive marine acoustic signals,
with no system reprogramming or retraining, recognize these signals as distinct
from the original set and distinguish them from one another; (c) Given a new 
training set of data on underwater echoes from active sonar returns, with
system reprogramming and/or retraining, modify the system to detect and 
classify each signal type in this new class of signals and distinguish them
from the original set of acoustic signals.  (4) Seismic signal discrimination:
(a) Given a training set of seismic signals (and associated parameters) from
different types of seismic events of varying magnitudes, each event recorded
at two or more seismic stations with ground truth provided, classify (as to
signal type), locate, and estimate the magnitude of similar events in a test
set of seismic signals (outside the training set); (b) Given one or more new
types of seismic signals, recognize these signals as distinct from the original
set (with no system reprogramming or retraining); (c) Given a new training
set of seismic signals from seismic stations located in different geological 
regions from the original stations, with system reprogramming and/or retrain-
ing, modify the system to classify and characterize this new set of signals.
The criteria for evaluating the performance of the classification systems will
include: (a) Classification accuracy (the appropriate accuracy metric for the
task addressed, e.g., percentage or correct detections, identifications, and/or
classifications, including false alarms where applicable; or total error 
rates); (b) System development time (the time required to develop and train the
system); (c) Fault tolerance (the percentage of original performance when 
subjected to failure of some of the processing elements); (d) Generality (the
accuracy of the system for new input data significantly outside the range of
training data); (e) Adaptability (the time and effort required to modify the
system to address similar classification problems with different classes of
data); (f) Computational efficiency (the period solution speed when optimally
implemented in hardware); (g) Size and power requirements (the projected
size and power requirements of the computational hardware); (h) Performance
vs training data (the rate of improvement in performance with increasing size of
the training data set).  This effort is a part of the DARPA program on Neural
Networks, the total funding for which is anticipated to be $33M over a 28
month period.  Proposals for projects covering less than 28 months are 
encouraged.  Proposals may be submitted any time through 4PM, March 1, 1989.
The proposal must contain the information listed below.  (1) The name, address,
and telephone number of the individual or organization submitting the proposal;
(2) A brief title that clearly identifies the application being addressed,
a concise descriptive summary of the proposed research, a supporting detailed
statement of the technical approach, and a description of the facilities to
be employed in this research.  Cooperative arrangements among industries,
universities, and other institutions are encouraged whenever this is 
advantageous to executing the proposed research.  Proprietary portions to
the technical proposal should be specifically identified.  Such proprietary
information will be treated with strict confidentiality; (3) The names, titles,
and proposed roles of this principal investigators and other key personnel
to be employed in the conduct of this research, with brief, resumes
that describe their pertinent accomplishments and publications; (4) A cost
proposal on SF1411 (or its equivalent) describing total costs, and an itemized
list of costs for labor, expendable and non-expendable equipment and
supplies, travel, subcontractors, consultants, and fees; (5) A schedule 
listing anticipated spending rates and program milestones; (6) The signature
of the individual (if applying on his own behalf) or of an official duly
authorized to commit the organization in business and financial affairs.  
Proposals should address a single application.  The technical content of the
proposals is not to exceed a total of 15 pages in length (double-spaced, 8 1/2
x 11 inches), exclusive of figures, tables, references, resumes, and cost
proposal.  Proposals should contain a statement of validity for at least
150 days beyond the closing date of this announcement.  Evaluation of proposals
received in response to the BAA will be accomplished through a peer or
scientific review.  Selection of proposals will be based on the following 
evaluation criteria, listed in descending order of relative importance;
(1) Contribution of the proposed work to the stated objectives of the
program; (2) The soundness of the technical approach; (3) The uniqueness
and innovative content; (4) The qualifications of the principal and supporting
investigators; (5) The institution's capabilities and facilities; and (6) The
reasonableness of the proposed costs.  Selection will be based primarily on 
scientific or technical merit, importance to the program and fund availability.
Cost realism and reasonableness will only be significant in deciding between 
two technically equal proposals.  Fifteen copies of proposals should be sub-
mitted to:  Barbara L. Yoon, DARPA/DSO, 1400 Wilson Blvd., 6th Floor, 
Arlington, VA  2209-2308.  Technical questions should be addressed to Dr. Yoon,
telephone (202)694-1303.  This CBD notice itself constitutes the Broad
Agency Announcement as contemplated in FAR 6.102(d)(2).  No additional written
information is available, nor will a formal RFP or other solicitation
regarding this announcement be issued.  Requests for same will be disregarded.
The Government reserves the right to select for award all, some or none of the
proposals received in response to this announcement.  All responsible sources
may submit a proposal which shall be considered by DARPA.