AI 中文总结
本研究提出逆混合整数优化框架，联合学习专家决策者的偏好与决策规则，通过三类案例验证其能更准确预测专家决策，为可解释建模提供新方法。
AI 中文摘要
理解专家如何做出决策并能转移该知识至关重要，尤其在复杂工程应用中，这对培训新手、提升人机系统性能，以及潜在实现与人类专家表现相当的完全自主系统具有重要价值。然而，专家通过多年经验形成的决策策略往往无法直接获取，因为其中涉及的隐性偏好和决策规则难以明确指定。这促使人们利用专家的观测决策来学习能捕捉其决策过程的可解释模型。本研究开发了一种逆优化方法，用于联合学习决策者的偏好（或感知成本）和支配其选择的决策规则。我们通过三个案例研究展示了该方法的通用性，分别考虑轮班分配问题、生产计划问题和真实世界路径规划问题。在这些案例研究中，同时建模感知成本和决策规则可实现更准确的预测，凸显了所提框架的价值及其在捕捉和复制专家决策方面的更大灵活性。
英文摘要
Understanding how experts make decisions and being able to transfer that knowledge is important, especially in complex engineering applications. It is highly valuable for training novices, improving the performance of human-machine systems, and potentially enabling fully autonomous systems that perform as well as human experts. However, an expert's decision-making strategy, developed through years of experience, is often not directly accessible, since the implicit preferences and decision rules involved can be difficult to specify explicitly. This has motivated the use of observed decisions made by the expert to learn an interpretable model that captures the expert's decision-making process. In this work, we develop an inverse optimization approach to jointly learn the decision-maker's preferences (or perceived costs) and the decision rules governing their choices. We demonstrate the general applicability of our approach using three case studies that consider a shift assignment problem, a production planning problem, and a real-world routing problem, respectively. Across these case studies, modeling both perceived costs and decision rules leads to better predictions, highlighting the value of the proposed framework and its greater flexibility in capturing and replicating expert decision making.
