Artificial Intelligence (AI) planning is a flourishing research and development discipline that provides powerful tools for searching a course of action that achieves some user goal. While these planning tools show excellent performance on benchmark planning problems, they represent challenging software systems when it comes to their use and integration in real-world applications. In fact, even in-depth understanding of their internal mechanisms does not guarantee that one can successfully set up, use and manipulate existing planning tools. We contribute toward alleviating this situation by proposing a service-oriented planning architecture to be at the core of the ability to design, develop and use next-generation AI planning systems. We collect and classify common planning capabilities to form the building blocks of the planning architecture. We incorporate software design principles and patterns into the architecture to allow for usability, interoperability and reusability of the planning capabilities. Our prototype planning system demonstrates the potential of our approach for rapid prototyping and flexibility of system composition. Finally, we provide insight into the qualitative advantages of our approach when compared to a typical planning tool.
Planning is concerned with the automated solution of action sequencing problems described in declarative languages giving the action preconditions and effects. One important application area for such technology is the creation of new processes in Business Process Management (BPM), which is essential in an ever more dynamic business environment. A major obstacle for the application of Planning in this area lies in the modeling. Obtaining a suitable model to plan with -- ideally a description in PDDL, the most commonly used planning language -- is often prohibitively complicated and/or costly. O…
Practically all of the planning research is limited to states represented in terms of Boolean and numeric state variables. Many practical problems, for example, planning inside complex software systems, require far more complex data types, and even real-world planning in many cases requires concepts such as sets of objects, which are not convenient to express in modeling languages with scalar types only. In this work, we investigate a modeling language for complex software systems, which supports complex data types such as sets, arrays, records, and unions. We give a reduction of a broad range…
Modern software systems increasingly incorporate self-* behavior to adapt to changes in the environment at runtime. Such adaptations often involve reconfiguring the software architecture of the system. Many systems also need to manage their architecture themselves, i.e., they need a planning component to autonomously decide which reconfigurations to execute to reach a desired target configuration. For the specification of reconfigurations, we employ graph transformations systems (GTS) due to the close relation of graphs and UML object diagrams. We solve the resulting planning problems with a p…
Planning is an essential topic in the realm of automated driving. Besides planning algorithms that are widely covered in the literature, planning requires different software tools for its development, validation, and execution. This paper presents a survey of such tools including map representations, communication, traffic rules, open-source planning stacks and middleware, simulation, and visualization tools as well as benchmarks. We start by defining the planning task and different supporting tools. Next, we provide a comprehensive review of state-of-the-art developments and analysis of relat…
LensKit is an open-source toolkit for building, researching, and learning about recommender systems. First released in 2010 as a Java framework, it has supported diverse published research, small-scale production deployments, and education in both MOOC and traditional classroom settings. In this paper, I present the next generation of the LensKit project, re-envisioning the original tool's objectives as flexible Python package for supporting recommender systems research and development. LensKit for Python (LKPY) enables researchers and students to build robust, flexible, and reproducible exper…
Beyond the Next Step: Variable-Length Latent World Models for Long-Horizon Planning
State Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院通用人工智能国家重点实验室）；Amazon AGI SF Lab（亚马逊AGI旧金山实验室）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models
School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统全国重点实验室）；Center for Artificial Intelligence and Robotics, HKISI, CAS（中国科学院香港创新研究院人工智能与机器人创新中心）；The Hong Kong Polytechnic University（香港理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Leveraging Taxonomy Similarity for Next Activity Prediction in Patient Treatment
German Reseach Centre for Artificial Intelligence (DFKI), SDS Branch Trier（德国人工智能研究中心（DFKI），特里尔分部）；University of Trier, Business Information Systems II（特里尔大学，商业信息系统二系）
Rice University（莱斯大学）；Shanghai Jiao Tong University（上海交通大学）；National University of Singapore（新加坡国立大学）
Identifying internal parameters for planning is crucial to maximizing the performance of a planner. However, automatically tuning internal parameters which are conditioned on the problem instance is especially challenging. A recent line of work focuses on learning planning parameter generators, but lack a consistent problem definition and software framework. This work proposes the unified planner optimization problem (POP) formulation, along with the Open Planner Optimization Framework (OPOF), a highly extensible software framework to specify and to solve these problems in a reusable manner.
Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science
Argonne National Laboratory（阿贡国家实验室）；Emory University（埃默里大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；Team Libra（团队Libra）；Boston University（波士顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Sustainable Horizons Institute（可持续远景研究所）；Stony Brook University（石溪大学）；University of Montana（蒙大拿大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；Lawrence Berkeley National Laboratory（伯克利国家实验室）；Sandia National Laboratories（桑塔那国家实验室）；Center for Scientific Collaboration and Community Engagement（科学协作与社区参与中心）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Californi（加利福尼亚）
