--- M.I.F.T. --- 

This is the home page for the MIFT project in the Database Group of the Computer Science Department at Stanford University. MIFT is sponsored by the DARPA , Computer-Aided Education and Training Initiative (CAETI) program.


The MIFT R&D problem is to customize the feedback from training exercises by exploiting knowledge about the training scenario, training objectives, and specific student/teacher needs. We plan to achieve this by inserting intelligent mediators into the information flow from observations collected during training exercises to the display and user interface functionality. Knowledge about training objectives, scenerios, and tasks is maintained in the mediators. A technical constraint is that domain experts must be able to extend mediators by adding domain-specific knowledge that supports additional aggregations, abstractions, and views of the results of training exercises.

Here is a description and a demonstration of MIFT applied to simulation-based military training. MIFT's mediators are intended to integrate with existing military training exercise management tools and dramatically reduce the cost of developing and maintaining separate feedback and evaluation tools for every training simulator and every set of customer needs.


The "MIFT Architecture" is designed as a set of independently reusable components which interact with each through standardized KIF/KQML interfaces.

The MIFT "Technical Approach" is intended to integrate with other exercise management software and achieve two key application goals for exercice feedback software:

1. Since students, teachers, and exercise evaluators seldom have much time to learn to use exercise feedback and analysis software, the software must be easy to use and must use domain-specific exercise concepts and terminology already familar to these customers.

2. Domain experts must be able to extend feedback software and tailor it to domain-specific and local needs. Professional programmers should seldom be required when extending the feedback analyses, modifying them for additonal consumers, and transferring them for use with additional exercises.

The members of the MIFT project team are: Prof. Gio Wiederhold, Principal Investigator, Stanford University, Dr. Ted Linden, Project Manager from Myriad Software, Dr. David Maluf, Post Doctoral Research Scientist, Stanford University, Ms. Priya Panchapagesan, Graduate Research Assistant, Stanford University and Joshua Hui, Graduate Research Assistant, Stanford University.

This page and related demo are maintained by David Maluf.