CASE-BASED SELECTION OF ALLOCATION METHODS

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$55,452.00
Award Year:
1990
Program:
SBIR
Phase:
Phase I
Contract:
n/a
Agency Tracking Number:
12627
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
Klein Assocs Inc
Po Box 264 - 800 Livermore St, Yellow Springs, OH, 45387
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
Dr Gary A Klein
(513) 767-2691
Business Contact:
() -
Research Institution:
n/a
Abstract
THE OBJECTIVE IS TO DEVELOP A CASE-BASED REASONING (CBR) SYSTEM FOR DETERMINING THE ALLOCATION METHODS USED BY FIRE DIRECTION OFFICERS. CASE-BASED REASONING IS A NEW APPROACH TO EXPERT SYSTEMS; COMPARED TO RULE-BASED SYSTEMS, CBR APPEARS TO BE A MORE EFFICIENT, SENSITIVE, AND EFFECTIVE STRATEGY TO DERIVE AND REPRESENT CRITICAL KNOWLEDGE. PHASE I WILL DEMONSTRATE THE FEASIBILITY OF A CBR APPROACH THAT USES TACTICAL FIRE CONTROL DECISION DATA FROM PREVIOUS EXERCISES AS THE BASIS FOR PREDICTIONS. STANDARD ARTIFICIAL INTELLIGENCE (AI) APPROACHES RUN INTO THE DIFFICULTY OF HAVING TO MODEL THE COMPLEXITIES OF THE HARDWARE/SOFTWARE ELEMENTS ALONG WITH THE COMPLEXITIES OF THE HUMAN FACTORS. A CBR STRATEGY AVOID THIS PROBLEM BY RELYING ON MATCHES TO SIMILAR PROJECTS SO THAT IT IS NOT NECESSARY TO REPRESENT WORLD KNOWLEDGE. ADDITIONALLY, A CBR APPROACH OFFERS THE POSSIBILITY OF IMPROVING REAL-TIME PERFORMANCE CONSIDERABLY, SINCE A SIMILAR CASE REPRESENTING A NEAR SOLUTION CAN BE RETRIEVED RAPIDLY.

* information listed above is at the time of submission.

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