任何 AI 增强业务流程管理系统 (ABPMS) 的核心组件都是流程框架,，它赋予系统流程意识并定义其最大行为边界。与传统流程模型, 相比，流程框架原则上, , 应该提供托管流程, 更宽松的表示，以便可以出现ABPMS, 的(semi) 自主行为（称为框架自治,）。此外, 流程框架不限于单一语言或符号形式，并且可以包含从预定义程序到常识规则和最佳实践的异构知识。在本文,中，我们首先将ABPMS流程框架概念化为混合业务流程表示,，由半并发执行的程序性和声明性流程模型,组成，将声明性范式的开放世界假设也扩展到程序模型。后者允许组合任何类型的任何(不冲突)模型来执行,，但使从事件数据自动发现这些模型变得复杂。现有的程序模型方法尤其受到影响，因为它们依赖于直接观察活动对之间的关系。为了寻找替代,，我们深入分析了不同的过程行为如何表现为一组已发现的声明约束,，每个约束都对应于特定类型的最终遵循关系。这揭示了声明性模型和过程性模型, 之间的行为重叠，同时也为开发相应的过程(frame) 发现技术奠定了基础。

A core component of any AI-Augmented Business Process Management System (ABPMS) is the process frame, which gives the system process-awareness and defines its maximal behavioral boundaries. Compared to traditional process models, the process frame should, in principle, provide a somewhat more permissive representation of the managed processes, such that the (semi) autonomous behavior of an ABPMS, referred to as framed autonomy, could emerge. In addition, the process frame is not limited to a single linguistic or symbolic formalism and may incorporate heterogeneous knowledge ranging from predefined procedures to common sense rules and best practices. In this paper, we first conceptualize the ABPMS process frame as a hybrid business process representation, consisting of semi-concurrently executed procedural and declarative process models, extending the open-world assumption of the declarative paradigm also to procedural models. The latter allows any set of (non-conflicting) models of either type to be combined for execution, but complicates the automated discovery of these models from event data. Existing approaches for procedural models are particularly affected due to their reliance on observing directly-follows relations between pairs of activities. In search of an alternative, we present an in-depth analysis of how different procedural behaviors manifest as sets of discovered Declare constraints, each corresponding to a specific type of eventually-follows relation. This reveals behavioral overlaps between declarative and procedural models, while also laying the foundation for developing corresponding process (frame) discovery techniques.

科目:人工智能(cs.AI)

Subjects: Artificial Intelligence (cs.AI)