AI 中文总结
研究在人工智能增强业务流程管理系统中，通过引入新工具，用多视角约束增强流程框架，为部分流程执行推荐最优延续，经实证评估该技术具可扩展性和有效性，能支持自主及约束感知决策。
AI 中文摘要
人工智能增强的业务流程管理系统（ABPMS）利用先进的人工智能技术来定义、执行和监控复杂的流程结构，从而增强传统的BPMS。在此背景下，框架自主性表示系统在严格遵守预定义框架（即一组可能跨越多个视角的约束）的同时，自主推进业务流程（BP）实例执行的能力。现有关于框架自主性的研究主要集中在声明式或过程式的控制流约束上，通常依赖于将其转换为基于自动机的表示形式。在本研究中，我们通过引入一种用于假设分析的新工具来扩展这一工作，该工具用多视角约束（包括数据感知和时间条件）增强流程框架。给定部分流程执行情况，所提出的方法利用这个丰富的框架来推荐符合基础流程规范的最优延续。我们还报告了一项实证评估，证明了该技术的可扩展性和有效性，从而突出了其在ABPMS中支持自主和约束感知决策的潜力。
英文摘要
AI-Augmented Business Process Management Systems (ABPMS) enhance traditional BPMS by leveraging advanced AI techniques to define, execute, and monitor complex process structures. Within this landscape, Framed Autonomy denotes the capability of a system to autonomously advance the execution of a Business Process (BP) instance while strictly adhering to a predefined frame, i.e., a set of constraints that may span multiple perspectives. Existing research on framed autonomy has predominantly focused on control-flow constraints, either declarative or procedural, and typically relies on their transformation into automata-based representations. In this study, we extend this line of work by introducing a novel tool for what-if analysis that augments the process frame with multi-perspective constraints, including data-aware and temporal conditions. Given a partial process execution, the proposed approach exploits this enriched frame to recommend optimal continuations in compliance with the underlying process specifications. We additionally report an empirical evaluation demonstrating the scalability and effectiveness of the technique, thereby highlighting its potential for supporting autonomous and constraint-aware decision making in ABPMS.
