A Combined Simulation and Machine Learning Method To Modelling People's Behaviour During Demonstrations and Protests - Dariusz PIERZCHALA (Communications of International Proceedings, 2021)
In recent months, because of epidemiological restrictions and as women reaction to the government decisions resulting in limitations of freedom and sovereignty, the women’s protest has quickly morphed from one against the abortion ban into a protest against the government. The police were not well prepared to respond to such protesters and there were many violations of the law from both sides. As for now, in the Polish Police, training and analyses have been conducted without advanced simulation tools. The proposed combined method based on variable resolution simulators with the use of ML techniques (as a decision gaming environment) will make the education much more realistic, complete, and tailored to the current operational situations. M&S of dynamic structures, and particularly the behaviour of persons, should be performed at the level of detail which is adequate to the problem and modelling purpose defined. This is the main and direct reason for the application of the Multi-resolution Agent Model (MrAM) approach in the simulation with functions for a state’s transformation (aggregation/disaggregation). The paper presents a programmable distributed simulation environment with the use of combined simulation and Machine Learning methods to modelling people's behaviour during demonstrations and protests. The purpose was to apply the combined methods (simulation and ML) in a single distributed MRM simulation environment and thus obtain better results than before. It is legitimate by the facts that Machine learning and simulation have a similar goal - predicting the behaviour of an object with mathematical modelling and data analysis.
Keywords: Crowd Simulation; Machine Learning; Reinforcement Learning; Multiresolution Simulation