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ARTIFICIAL INTELLIGENCE FOR COMMAND AND CONTROL

Award Information

Agency:
Department of Defense
Branch:
Army
Award ID:
3824
Program Year/Program:
1988 / SBIR
Agency Tracking Number:
3824
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Decision Science Associates
P.O. Box 969 Vienna, VA 22183 0096
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Woman-Owned: No
Minority-Owned: Yes
HUBZone-Owned: No
 
Phase 2
Fiscal Year: 1988
Title: ARTIFICIAL INTELLIGENCE FOR COMMAND AND CONTROL
Agency / Branch: DOD / ARMY
Contract: N/A
Award Amount: $500,000.00
 

Abstract:

THE MODERN BATTFIELD CALLS FOR HIGH-STAKES DECISIONS AND JUDGMENTS IN AN INCREASINGLY COMPLEX ENVIRONMENT CHARACTERIZED BY HIGH VOLUMES OF UNCERTAIN, INCOMPLETE, AND OFTEN INCONSISTENT INFORMATION. IN THE FUTURE, INTELLIGENT COMPUTERIZED SYSTEMS CAPABLE OF ADAPTIVE LEARNING WILL BECOME NECESSARY AS AIDS TO HUMAN DECISION MAKERS. DSC HYPOTHESIZES THAT SIGNIFICANT ADVANCES IN THE CAPABILITY FOR TRUE ADAPTIVE LEARNING CAN BE ACHIEVED BY COMBINING A GOAL-BASED, HIERARCHIAL KNOWLEDGE REPRESENTATION WITH A STATE-OF-THE-ART CAPABILITY FOR REPRESENTING AND MANIPULATING UNCERTAINTY. THIS COMBINATION WILL MAKE POS-SIBLE A SYNERGISTIC LEAP IN THE FLEXIBILITY AND ADAPTABILITY OF INTEL-LIGENT BATTLEFIELD EXPERT SYSTEMS. FOUR TASKS ARE PROPOSED FOR PHASE I: (1) DEVELOP A THEORY OF HIERARCHICAL, GOAL-STRUCTURED KNOWLEDGE REPRESENTATION; (2) DEVELOP THEORIES FOR REPRESENTING AND MANIPULATING UNCERTAINTY; (3) SYNTHESIZE THEORIES OF KNOWLEDGE REPRESENTATION AND UNCERTAINTY INTO DESIGNS FOR EXPERT SYSTEM ARCHITECTURE; AND (4) IMPLEMENT SELECTED IDEAS IN A SMALL-SCALE PROTOTYPE SYSTEM.

Principal Investigator:

Dr Marvin S Cohen
7037900510

Business Contact:

Small Business Information at Submission:

Decision Science Consortium
7700 Leesburg Pike - Ste 421 Falls Church, VA 22043

EIN/Tax ID:
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No