A BDI Modeling Approach for Decision Support in Supply Chain Quality Inspection
提出一种信念-愿望-意图建模方法，通过模拟供应商的推理过程预测其欺骗意图，为买方提供检验建议，实验表明该方法比经典机器学习更准确。
Quality inspection, a widely adopted practice in supply chains, measures whether delivered products conform to prespecified quality requirements. Due to potential economic benefits, suppliers may deliberately manipulate products to falsify test results. However, unqualified products could cause severe problems in supply chains or even tragedies, such as China's tainted milk scandal. In this paper, we propose a beliefdesire-intention modeling approach to predict suppliers' behavior and provide inspection suggestions to buyers to overcome the problem. Assuming access to the production process, our approach can represent suppliers' knowledge of production and deception to mimic their reasoning processes and predict their deception intentions. This flexible approach can also adapt to environmental changes and deliver effective results. In this paper, we build a prototype system for supply chain quality inspections based on the proposed method. We conduct laboratory experiments to collect data for computational assessments of the performance of the prototype. It is shown that our proposed approach is more accurate than classic machine learning methods in detecting suppliers' deceptions.