Mediation to Implement Feedback in Training (MIFT)


Gio Wiederhold, Principal Investigator

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;

David Maluf, CS Post Doctoral Research Scientist

Computer Science Department, Stanford University, Gates 4A, Stanford CA 94305

Bhujanga Priya Panchapagesan, EE MS student; Gates B28.

Joshua Hui, CS MS student (part time); Gates B24.


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.


Mediator technology to analyze data obtained during simulated, real, and mixed training exercises according to training scenarios.


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.


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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