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LEARNING SYSTEMS FOR ELECTRONIC COMBAT APPLICATIONS (LSECA)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: N/A
Agency Tracking Number: 8221
Amount: $57,858.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 1988
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
1615 Poes Ln
Charlottesville, VA 22901
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 GERARD J MONTGOMERY
 (804) 973-7395
Business Contact
Phone: () -
Research Institution
N/A
Abstract

ADVANCES IN ELECTRONIC COMBAT APPLICATIONS WILL SIGNIFICANTLY INCREASE THE ABILITY OF PILOTS TO OUTPERFORM A LARGE NUMBER OF ADVERSARIES IN ENVIRONMENTS WHERE THERE IS AN EXTREME INFORMATION PROCESSING BURDEN. THE NUMBER OF FACTORS PILOTS MUST CONSIDER IN MAKING ELECTRONIC COMBAT RELATED DECISIONS IS VERY HIGH AND OFTEN INVOLVE UNKNOWN RELATIONSHIPS THAT MUST BE LEARNED FROM EXPERIENCE. IN ADDITION, MUCH OF THE DATA IS UNRELIABLE OR MISSING, RESULTING IN A SUBSTANTIAL AMOUNT OF UNCERTAINTY. THIS PROPOSAL DISCUSSES A NEW FORM OF REASONING CALLED ABDUCTIVE REASONING AND RELATED INDUCTIVE LEARNING ALGORITHMS THAT PROVIDE A GENERAL MEANS TO ATTAIN SATISFACTORY SOLUTIONS TO PROBLEMS THAT CAN NOT BE RESOLVED USING CURRENT COMPUTER SCIENCE METHODS. THE LEARNING ALGORITHMS ARE BASED ON POLYNOMIAL NETWORK MODELING TECHNIQUES WHICH OFFER TREMENDOUS NEARTERM POTENTIAL IN AUTOMATICALLY SYNTHESIZING EFFECTIVE DECISION MODELS TO DEAL WITH THE UNCERTAINTIES ASSOCIATED WITH ELECTRONIC COMBAT APPLICATIONS, AND PROVIDE THE MEANS TO OBTAIN SATISFACTORY AND PRACTICAL REAL-TIME SOLUTIONS TO SUCH COMPLEX PROBLEMS.

* Information listed above is at the time of submission. *

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