Mediation to Implement Feedback in Training (MIFT)
Participants:
Computer Science Department, Stanford University, Gates 4A, Stanford CA 94305
Ted Linden, Manager
Myriad Software; 2245 Tasso St., Palo Alto, CA 94301
Phone: 415-327-2973; FAX: 415-327-5509; linden@MyriadSfw.com
David Maluf, CS Post Doctoral Research Scientist
Computer Science Department, Stanford University, Gates 4A, Stanford CA 94305
Bhujanga Priya Panchapagesan, EE MS student
priya@db.stanford.edu; Gates B28.
Joshua Hui, CS MS student (part time)
wjhui@cs.stanford.edu; Gates B24.
Funding
We are supported by DARPA ISO / CAETI, as part of the Exman Project.
Funding started March 1 1996.
Kirstie Bellman is the DARPA program Manager.
The first phase of this project has been completed. Continuation is dependendent
on further funding.
Description
Mediator technology to analyze data obtained during simulated, real,
and mixed training exercises according to training scenarios.
Abstract
This project performs research in automated data abstraction,
based on a formalized model of the customer's need for information.
Such an abstraction process will be performed by a knowledge-driven
subsystem
in a computer network which mediates between customers and
data resources. The aproach focuses on the crucial issue of data or
information overload, which occurs when the volume of data exceeds
what a customer can comprehend. This problem is increasing in
importance, since improved communication, larger databases, and
effective search methods are now providing more material than people
can afford to read or analyze.
The specific application is to training data, and the model
represents the learning/training scenario. A scenario is intended to
fullfill a number of training objectives. After the scenario is
executed (and perhaps even during excution) the feedback can help
design better or complementary successor scenarios.
Mediators for
training data;
Mediators
for training data (gif)
Information
Flow.
Work Plan
Stanford University is developing a mediator-based software architecture
for the Exercise Analysis and Feedback phase and for the feedback loop to
exercise planning and preparation. The mediators incorporate knowledge
about the scenario objectives and the task and subtasks to be trained.
Mediators use this scenario knowledge to relate simulation results to the
objectives and tasks to be trained so that O/Cs, trainees, and commanders
can query the simulation results using normal scenario-based terminology.
For example, rather than forcing the O/C formulate a query to "select all
enemy detections of Alpha company before it began its attack," the O/C
will simply ask whether Alpha company achieved its scenario subtask of
remaining hidden until the beginning of the attack. A mediator will know
that enemy detections before the attack are evidence that the unit was not
successful in remaining hidden. Mediators will produce results tailored to
various needs including those of exercise planners, weapons designers,
tactics developers, and other consumers of simulation results.
A second goal of the mediator-based architecture is that military training
and support personnel will tailor and extend the analysis and feedback
software to meet there own local needs. The goal is to dramatically reduce
the amount of contract programming needed to develop separate analysis and
feedback software for each simulator and each consumer of the simulation
results.
Reference
Maluf David A., Wiederhold Gio, Linden Ted and Panchapagesan, Priya,
"Mediation
to Implement Feedback in Training," abstract to appear in: CrossTalk:
Journal of Defense Software Engineering, Software Technology
Support Center, Department of Defense, 1997.
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