Improved Effective Management of the Uncertainty in Army Decision Making using Cognitive Agents, Classification based on Fuzzy Association Rules and Genetic Rule Selection

Abstract

Decision making (DM) is an important problem in most of the armyoperations. One of the challenging issues in this area is uncertainty in warswith uncertain information which causes many destructive effects on theresults of strategies in battlefields. In the Heravi et al. article’s, published inthe year 2013, utilizing a combination of Cognitive Agent (CA) andClassification based on Fuzzy Association Rules (CFAR) as the mosteffective and widely used methods, was able to relatively reduce thisproblem and tried to reduce uncertainty. But still in critical condition, can’tdeny the need to act quickly and remove most invalid and inefficient rulesextracted in the effective decisions.This paper aims to utilize the capabilities of Genetic Algorithm (GA) in amore realistic selection rules as a meta-heuristic way to combinecomplementary methods to minimize the uncertainty in DM. In comparisonwith previous method, experimental results achieved, clearly show that thiscombination in addition to the advantages of the previous method, due to thefurther reduction of production rules for DM, are more understandable andaccurate and has more rational risk acceptance.

Keywords

• decision making
• uncertainty management
• asymmetric warfare
• cognitive agent
• classification based on fuzzy association rules
• genetic rule selection