Chemical production activities in industrial districts pose great threats to the surrounding atmospheric environment and human health. Therefore, developing appropriate and intelligent pollution controlling strategies for the management team to monitor chemical production processes is significantly essential in a chemical industrial district. The literature shows that playing a chemical plant environmental protection (CPEP) game can force the chemical plants to be more compliant with environmental protection authorities and reduce the potential risks of hazardous gas dispersion accidents. However, results of the current literature strictly rely on several perfect assumptions which rarely hold in real-world domains, especially when dealing with human adversaries. To address bounded rationality and limited observability in human cognition, the CPEP game is extended to generate robust schedules of inspection resources for inspection agencies. The present paper is innovative on the following contributions: (i) The CPEP model is extended by taking observation frequency and observation cost of adversaries into account, and thus better reflects the industrial reality; (ii) Uncertainties such as attackers with bounded rationality, attackers with limited observation and incomplete information (i.e., the attacker’s parameters) are integrated into the extended CPEP model; (iii) Learning curve theory is employed to determine the attacker’s observability in the game solver. Results in the case study imply that this work improves the decision-making process for environmental protection authorities in practical fields by bringing more rewards to the inspection agencies and by acquiring more compliance from chemical plants.
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