Manually creating Planning Domain Definition Language (PDDL) descriptions is difficult, error-prone, and requires extensive expert knowledge. However, this knowledge is already embedded in engineering models and can be reused. Therefore, this contribution presents a comprehensive workflow for the automated generation of PDDL descriptions from integrated system and product models. The proposed workflow leverages Model-Based Systems Engineering (MBSE) to organize and manage system and product information, translating it automatically into PDDL syntax for planning purposes. By connecting system and product models with planning aspects, it ensures that changes in these models are quickly reflected in updated PDDL descriptions, facilitating efficient and adaptable planning processes. The workflow is validated within a use case from aircraft assembly.
From Capability Models to Automated Planning: An AAS-Native Approach for Automatic PDDL Generation
Institute of Automation Technology, Helmut Schmidt University Hamburg（海德堡-施密特大学汉堡自动化技术研究所）；Chair of Automation Technology, Ruhr University Bochum（博尔塔伦大学博德姆自动化技术教授团）
Aligning LLM+PDDL Symbolic Plans with Human Objective Specifications through Evolutionary Algorithm Guidance
College of Engineering and Computer Science, University of Central Florida（中央佛罗里达大学工程与计算机科学学院）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
A Sober Look at Agentic Misalignment in Automated Workflows
University of Virginia（弗吉尼亚大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Utah（犹他大学）；University of Notre Dame（圣约翰大学）；George Washington University（乔治华盛顿大学）
Automated Lesion Segmentation in Whole-Body PET/CT in a multitracer setting
Shanghai United Imaging Healthcare Advanced Technology Research Institute Co., Ltd.（上海联影医疗科技股份有限公司先进技术研究院）；Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）
It`s All About Speed: AI`s Impact on Workflow in Music Production
Waipapa Taumata Rau - University of Auckland, Auckland (Aotearoa - New Zealand)（瓦伊帕塔玛拉大学——奥克兰大学，奥克兰（奥特亚罗——新西兰））；Sony AI, Barcelona (Spain)（索尼AI，巴塞罗那（西班牙））
HyEvo: Self-Evolving Hybrid Agentic Workflows for Efficient Reasoning
East China Normal University（华东师范大学）；Beihang University（北京航空航天大学）；Shanghai University of International Business and Economics（上海对外经贸大学）；Fudan University（复旦大学）
CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows
Max Planck Institute for Dynamics and Self-Organization（马克斯·普朗克动力学与自组织研究所）；Georg-August-Universität（格奥尔格·奥古斯特大学）；German Center for Cardiovascular Research (DZHK)（德国心血管研究中心（DZHK））；University Medical Center Göttingen（哥廷根大学医学中心）；Northeastern University（东北大学）
RefGrader: Automated Grading of Mathematical Competition Proofs using Agentic Workflows
Pennsylvania State University（宾夕法尼亚州立大学）；City University of New York（纽约市立大学）；New York University（纽约大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；Autodesk（欧特克公司）；Carnegie Mellon University（卡内基梅隆大学）
WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks
College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港理工大学电子与计算机工程系）
DeepWisdom（深度智慧）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Renmin University of China（中国人民大学）；Nanjing University（南京大学）；Fudan University（复旦大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Mila（米拉研究所）；The Hong Kong University of Science and Technology（香港科技大学）
Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization
School of Artificial Intelligence, Nanjing University of Information Science and Technology（南京信息工程大学人工智能学院）；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Harnessing LLMs for Automated Video Content Analysis: An Exploratory Workflow of Short Videos on Depression
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Institute of High Performance Computing (IHPC), A*STAR（高性能计算研究所（A*STAR））；Pennsylvania State University（宾夕法尼亚州立大学）；Clark University（克拉克大学）；Columbia University（哥伦比亚大学）
SPOGW: a Score-based Preference Optimization method via Group-Wise comparison for workflows
Hangzhou International Innovation Institute and , Beihang University（杭州国际创新研究院和 北航）；School of Artificial Intelligence, Beihang University（人工智能学院，北航）；Nanyang Technological University（南洋理工大学）；University of Leicester（莱斯特大学）；China Mobile Communications Company Limited Research Institute（中国移动通信有限公司研究院）
