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

Decision making (DM) is an important problem in most of the army operations. One of the challenging issues in this area is uncertainty in wars with uncertain information which causes many destructive effects on the results of strategies in battlefields. In the Heravi et al. article’s, published in the year 2013, utilizing a combination of Cognitive Agent (CA) and Classification based on Fuzzy Association Rules (CFAR) as the most effective and widely used methods, was able to relatively reduce this problem and tried to reduce uncertainty. But still in critical condition, can’t deny the need to act quickly and remove most invalid and inefficient rules extracted in the effective decisions. This paper aims to utilize the capabilities of Genetic Algorithm (GA) in a more realistic selection rules as a meta-heuristic way to combine complementary methods to minimize the uncertainty in DM. In comparison with previous method, experimental results achieved, clearly show that this combination in addition to the advantages of the previous method, due to the further reduction of production rules for DM, are more understandable and accurate and has more rational risk acceptance.