Integrated Genetic Algorithmic and Fuzzy Logic Approach for Decision Making of Police Force Agents in Rescue Simulation Environment

Abstract

The major task of police force agents in rescue simulation environment is to connect the isolated parts of the city. To achieve this goal, the best blocked roads should be chosen to clear. This selection is based on some issues such as number of burning buildings and victims existing in the mentioned parts. A linear combination of these factors is essential to determine a priority for each road. In this paper we propose an integrated Genetic Algorithm (GA) and Fuzzy Logic approach to optimize the combination statement. The parameters are learned via GA for some training maps. Then, because of differences between test and train maps, the agent should decide which parameters to choose according to the new map. The agents' decision is based on similarity measures between characteristics of maps using Fuzzy Logic. After utilizing this method, the simulation score increased between 2% and 7% in 20 test maps.