通过时间背景知识引导预测过程监控中的反事实解释生成
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
AI总结：
针对预测过程监控中反事实解释未考虑时间背景知识的问题，调整基于遗传算法的反事实生成技术，使其满足时间约束，生成更合规且不损失传统质量指标的反事实解释。
摘要：
反事实解释用于说明输入实例中哪些部分需要改变才能改变AI系统的输出结果。然而，在预测过程监控领域处理反事实解释时，必须仔细考虑事件之间的控制流关系。实际上，反事实解释不应违反活动之间的控制流关系（时间背景知识）。在预测过程监控的可解释性领域中，已有一系列关于基于结果预测的反事实解释的研究工作，但这些工作在生成反事实解释时均未考虑纳入时间背景知识。在本研究中，我们对XAI领域中基于遗传算法的反事实生成最先进技术进行了调整，使其在运行时考虑一系列时间约束。我们假设时间背景知识是已知的，并对适应度函数以及交叉和变异算子进行了调整，以保持对约束的满足。我们针对反事实生成的最先进遗传算法对所提出的方法进行了评估，并呈现了结果。我们展示了纳入时间背景知识能够生成更符合时间背景知识的反事实解释，同时不会损失反事实的传统质量指标。
英文摘要：
Counterfactual explanations suggest what should be different in the input instance to change the outcome of an AI system. When dealing with counterfactual explanations in the field of Predictive Process Monitoring, however, control flow relationships among events have to be carefully considered. A counterfactual, indeed, should not violate control flow relationships among activities (temporal background knowledege). Within the field of Explainability in Predictive Process Monitoring, there have been a series of works regarding counterfactual explanations for outcome-based predictions. However, none of them consider the inclusion of temporal background knowledge when generating these counterfactuals. In this work, we adapt state-of-the-art techniques for counterfactual generation in the domain of XAI that are based on genetic algorithms to consider a series of temporal constraints at runtime. We assume that this temporal background knowledge is given, and we adapt the fitness function, as well as the crossover and mutation operators, to maintain the satisfaction of the constraints. The proposed methods are evaluated with respect to state-of-the-art genetic algorithms for counterfactual generation and the results are presented. We showcase that the inclusion of temporal background knowledge allows the generation of counterfactuals more conformant to the temporal background knowledge, without however losing in terms of the counterfactual traditional quality metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
