Achieving framed autonomy in AI-augmented business process management systems through automated planning

IF 3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Systems Pub Date : 2025-06-02 DOI:10.1016/j.is.2025.102573

Giacomo Acitelli , Anti Alman , Fabrizio Maria Maggi , Andrea Marrella

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引用次数: 0

Abstract

AI-augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems empowered by Artificial Intelligence (AI) technology for autonomously unfolding and adapting the execution flow of business processes (BPs) within a set of potentially conflicting procedural and declarative constraints, called process framing. In this respect, framed autonomy enables an ABPMS to autonomously decide how to progress the execution of a BP, as long as the boundaries imposed by the frame are respected. Among these constraints, there could be a partial BP execution that needs to be completed, activating a different near-optimal framing that enables the BP to progress its execution. In this paper, we present an automata-based technique that pairs constraint-based framing with automated planning in AI to recommend, given a partial BP execution trace, the continuation of that trace that minimizes the violation cost of the conforming space defined by the process frame. We report on the results of experiments of increasing complexity to showcase our technique’s performance and scalability.

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通过自动化规划在人工智能增强的业务流程管理系统中实现框架自治

人工智能增强业务流程管理系统（abpms）是一类新兴的流程感知信息系统，由人工智能（AI）技术支持，用于在一组潜在冲突的过程性和声明性约束（称为流程框架）中自主展开和调整业务流程（bp）的执行流。在这方面，框架自治使ABPMS能够自主决定如何推进BP的执行，只要框架施加的边界得到尊重。在这些约束条件中，可能存在需要完成的部分BP执行，激活不同的接近最优的帧，使BP能够继续执行。在本文中，我们提出了一种基于自动机的技术，该技术将基于约束的框架与人工智能中的自动规划相结合，在给定部分BP执行轨迹的情况下，推荐该轨迹的延续，从而最大限度地减少过程框架定义的一致性空间的违反成本。我们报告了增加复杂性的实验结果，以展示我们的技术的性能和可扩展性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Information Systems 工程技术-计算机：信息系统

CiteScore

9.40

自引率

2.70%

发文量

112

审稿时长

53 days

期刊介绍： Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems. Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.