AI-Augmented Business Process Management Systems: A Research Manifesto
University of Tartu（塔尔图大学）；Apromore；IBM Research - Haifa（IBM海法研究院）；Sapienza University of Rome（罗马第一大学（萨皮恩扎大学））；Free University of Bozen-Bolzano（博岑-波尔扎诺自由大学）；University of Mannheim（曼海姆大学）；Eindhoven University of Technology（埃因霍温理工大学）；Technion（以色列理工学院）
Marlon Dumas, Fabiana Fournier, Lior Limonad, Andrea Marrella, Marco Montali, Jana-Rebecca Rehse, Rafael Accorsi, Diego Calvanese, Giuseppe De Giacomo, Dirk Fah… 展开作者
Marlon Dumas, Fabiana Fournier, Lior Limonad, Andrea Marrella, Marco Montali, Jana-Rebecca Rehse, Rafael Accorsi, Diego Calvanese, Giuseppe De Giacomo, Dirk Fahland, Avigdor Gal, Marcello La Rosa, Hagen Völzer, Ingo Weber
备注：
19 pages, 1 figure
发表信息：
ACM Transactions on Management Information Systems, 31 January 2023 Volume 14, Issue 1, Article No.: 11, pp 1-19
AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics.
Agentic Business Process Management: A Research Manifesto
Free University of Bozen-Bolzano（博尔扎诺自由大学）；University of Oxford（牛津大学）；University of Tartu（塔尔图大学）；IBM Research（IBM研究院）；Umeå University（于默奥大学）；paluno (Ruhr Institute for Software Technology), University of Duisburg Essen（帕卢诺（鲁尔软件技术研究所），杜伊斯堡-埃森大学）；University of Ottawa（渥太华大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Saarland University（萨尔大学）；The University of Melbourne（墨尔本大学）；TUM School of Computation, Information, and Technology, TU Munich（慕尼黑工业大学计算、信息与技术学院）；RMIT University（皇家墨尔本理工大学）；Meta；University of St. Gallen（圣加仑大学）
From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
IBM Research - Israel（IBM研究院以色列分部）；Michael G. Foster School of Business, University of Washington（华盛顿大学迈克尔·G·福斯特商学院）；Allen School of Computer Science and Engineering, University of Washington（华盛顿大学艾伦计算机科学与工程学院）；Laboratory for Innovation Science at Harvard（哈佛大学创新科学实验室）；Amsterdam Business School, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹商学院）；MIT-IBM Watson AI Lab, IBM Research（IBM研究院麻省理工学院IBM沃森人工智能实验室）
Graph-Based Agentic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes
Dhillon School of Business, University of Lethbridge（莱斯布里奇大学迪隆商学院）；Universidad de Guadalajara（瓜达拉哈拉大学）；Al Hussein Technical University（侯赛因技术大学）；Universidad de Guanajuato（瓜纳华托大学）
Formal Foundations of Agentic Business Process Management
University of Oxford, UK（牛津大学，英国）；SAP, Germany（德国SAP公司）；Umeå University, Sweden（乌梅拉大学，瑞典）；University of Mannheim, Germany（曼海姆大学，德国）；Free University of Bozen-Bolzano, Italy（博兹纳自由大学，意大利）
Data Analytics Group, Haute Ecole Arc Ingénierie HES-SO, University of Applied Sciences Western Switzerland（瑞士西部应用科学大学西部应用科学大学阿尔科工程学院数据分析组）
Recent advances in the interconnectedness and digitization of industrial machines, known as Industry 4.0, pave the way for new analytical techniques. Indeed, the availability and the richness of production-related data enables new data-driven methods. In this paper, we propose a process mining approach augmented with artificial intelligence that (1) reconstructs the historical workload of a company and (2) predicts the workload using neural networks. Our method relies on logs, representing the history of business processes related to manufacturing. These logs are used to quantify the supply an…
University of Bamberg（班贝格大学）；ETH Zurich（苏黎世联邦理工学院）
The advance of Machine Learning (ML) has led to a strong interest in this technology to support decision making. While complex ML models provide predictions that are often more accurate than those of traditional tools, such models often hide the reasoning behind the prediction from their users, which can lead to lower adoption and lack of insight. Motivated by this tension, research has put forth Explainable Artificial Intelligence (XAI) techniques that uncover patterns discovered by ML. Despite the high hopes in both ML and XAI, there is little empirical evidence of the benefits to traditiona…
How well can a large language model explain business processes as perceived by users?
Eindhoven University of Technology（埃因霍温理工大学）；IBM Research（IBM研究院）；Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Joint Institute for Nuclear Research（联合核子研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）
On the Hybrid Nature of ABPMS Process Frames and its Implications on Automated Process Discovery
University of Tartu（塔尔图大学）；Free University of Bozen-Bolzano（博兹纳-博尔扎诺自由大学）；Bar-Ilan University（巴伊兰大学）；Technion – Israel Institute of Technology（技术学院-以色列理工学院）
The automation and digitalization of business processes has resulted in large amounts of data captured in information systems, which can aid businesses in understanding their processes better, improve workflows, or provide operational support. By making predictions about ongoing processes, bottlenecks can be identified and resources reallocated, as well as insights gained into the state of a process instance (case). Traditionally, data is extracted from systems in the form of an event log with a single identifying case notion, such as an order id for an Order to Cash (O2C) process. However, re…
An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring
Faculty of Engineering and Computer Science, University of Victoria, BC, Canada（工程与计算机科学学院，维多利亚大学）；Computer Science, Irving K. Barber Faculty of Science, University of British Columbia, BC, Canada（计算机科学，不列颠哥伦比亚大学理学院）
ORPR: An OR-Guided Pretrain-then-Reinforce Learning Model for Inventory Management
Department of Industrial Engineering, Tsinghua University（清华大学工业工程系）；Supply Chain Tech Team Y, JD.com（京东供应链技术团队）；Faculty of Engineering and Faculty of Business and Economics, The University of Hong Kong（香港大学工程学院和商学院）
