A fuzzy logic resource manager and underlying data mining techniques

Abstract

A fuzzy logic-based expert system has been developed that automatically allocates electronic attack (EA) resources in real-time over many dissimilar platforms. The platforms can be very general, eg. ships, planes, robots, land-based facilities, etc. The potential foes the platforms deal with can also be general. This paper describes data mining activities related to development of the resource manager, with a focus on genetic algorithm-based optimization. The use of a database of scenarios prevents the algorithm from having too narrow a range of behaviors, i.e. it creates a more robust solution. The approach to optimization is a type of co-evolution, i.e. both friend and foe agents simultaneously adapt within a complicated environment perceived through various sensors, such as radar, electronic support measures, etc. New rule classes and the five components of the resource manager are discussed. Finally, the resource manager's multi-platform response is examined for multiple scenarios.