  This paper presents a Multi-Agent System (MAS) approach for designing an air
pollution simulator. The aim is to simulate the concentration of air pollutants
emitted from sources (e.g. factories) and to investigate the emergence of
cooperation between the emission source managers and the impact this has on air
quality. The emission sources are controlled by agents. The agents try to
achieve their goals (i.e. increase production, which has the side effect of
raising air pollution) and also cooperate with others agents by altering their
emission rate according to the air quality. The agents play an adapted version
of the evolutionary N-Person Prisoners' Dilemma game in a non-deterministic
environment; they have two decisions: decrease or increase the emission. The
rewards/penalties are influenced by the pollutant concentration which is, in
turn, determined using climatic parameters. In order to give predictions about
the Plume Dispersion) model and an ANN (Artificial Neural Network) prediction
model. The prediction is calculated using the dispersal information and real
data about climatic parameters (wind speed, humidity, temperature and
rainfall). Every agent cooperates with its neighbours that emit the same
pollutant, and it learns how to adapt its strategy to gain more reward. When
the pollution level exceeds the maximum allowed level, agents are penalised
according to their participation. The system has been tested using real data
from the region of Annaba (North-East Algeria). It helped to investigate how
the regulations enhance the cooperation and may help controlling the air
quality. The designed system helps the environmental agencies to assess their
air pollution controlling policies.
