Breakthroughs in artificial intelligence are constantly reshaping the way we deal with complex challenges, and newly developed systems will further adopt these capabilities. Dr. Ilche Georgievski, Professor Marco Aiello of Stuttgart University and his team introduced a scalable hierarchical (SH) planning system, a user-friendly, open source system designed to plan step by step. Open source means that the system’s code is free to use and can be used, modified or improved by anyone. Their work, published in peer-reviewed Journal SoftwareX, highlights the importance of adaptability and efficiency in using artificial intelligence to solve real-world planning problems. Peer review means that other experts in the field have evaluated the research to ensure its quality and reliability.
Making effective plans with AI can be challenging, but SH simplifies the process. The system is built to work across different areas, making it a flexible tool to solve planning problems (such as decision making, coordination, assistance and control), generate step-by-step solutions, and connect with other digital systems. Unlike older AI planning tools, SH uses a modular design, which means users can adjust their functionality as needed. Modular design refers to a system composed of independent parts that can be replaced or upgraded without affecting the overall. “Our system provides an intelligent and flexible way to break down complex tasks into manageable steps while ensuring smooth integration with other applications,” explains Dr. Georgievski. The system was built using the Scala programming language, a high-level programming language known for its expression and modern efficiency, and is attractive to developers who value functional and object-oriented programming combinations.
Expression is one of the key advantages of SH. Unlike traditional planning tools that have limited support for dealing with real-world needs, SH supports various planning problems due to its ability to represent various characteristics of these problems (such as logic, digital, and time constraints). This expressive ability makes it particularly suitable for dealing with complex real-world areas. This makes it useful in various settings (such as smart buildings, organizations cloud-based applications) that are programs running on remote servers rather than on personal computers, or helping autonomous cars browse through their surroundings. The researchers tested SH against other well-known planning tools and found that it performs better in terms of speed and memory usage. Memory usage refers to the efficiency of the system processing and storing data. Professor Aiello said: “By designing it as a service that can be easily connected to other systems, SH is more than just a standalone tool, it is a valuable building block for large-scale AI projects.”
Ease of use is another major advantage of SH. The system is used with a specially designed planning language, which is a structured way to describe the problem so that the computer can generate solutions. This makes it easier to define the problem and find a solution. The structured way of processing information allows users to quickly convert abstract targets into actions. The design ensures that it can be applied to a wide range of planning issues such as decision-making, coordination, assistance and control, from managing smart buildings that use automated systems to control lighting, temperature and safety to simplify industrial operations that machines and systems work efficiently.
The developers also ensure that many users have access to SH. The system treats its functionality as a web service, allowing other applications or systems to interact with SH over the Internet by sending simple requests and receiving responses, for example, requiring planners to resolve specific planning issues. This means developers and researchers can quickly integrate it into their own projects without the need for extensive technical knowledge. “Our aim is to create an AI planning tool that is both powerful and easy to use in different situations,” said Dr. Georgievski. The system is structured to enable users to modify and expand their capabilities to suit their specific needs.
SH goes beyond the research environment and has the potential to improve many industries, including logistics, transportation and storage involving commodities, and coordination of robotics, involving the design and use of machines to perform tasks, and cloud-based services that enable users to access computing resources over the Internet. With its ability to handle complex planned tasks in an intelligent and efficient way, the system should play an important role in the future of automated solutions, referring to systems that perform minimal human input tasks. Because it is open source, developers around the world can contribute to its improvements, helping to create more advanced applications for AI in the coming years.
Journal Reference
Georgievski I., Palghadmal AV, Alnazer E., Aiello M. SoftwareX, 2024; 27:101779. doi:
About the Author
Marco Aiello He is a professor of computer science and head of the Department of Service Computing at the University of Stuttgart, Germany. The European Academy of Sciences and Arts, a global branch of Chang Gung University in Taipei, Taiwan, was elected. He is the vice president of Informatics Europe. He holds a PhD in logic from the University of Amsterdam, a Habilitation in Applied Informatics from Tu Wien and a Master of Engineering from La Sapienza University in Rome. In 2016, there were three former PhD students who founded the company’s sustainable construction BV, which was acquired by Dutch energy company Innova BV in 2020. His research interests are service computing, intelligent energy systems and spatial reasoning. He has written more than 200 peer-reviewed articles and several books that have been cited more than 8.000 times.
Ilche Georgievski It’s one private at the University of Stuttgart, Germany. He was born and raised in Bitola, Macedonia, and he embarked on an academic journey at the University of Maribor in Slovenia, where he earned his master’s degree in computer and information science. He went on to earn his Ph.D. He has a PhD in Computer Science from the University of Groningen, Netherlands in 2015 and continued to serve as a postdoctoral fellow until 2017. Between academic positions, he brought expertise into the industry and served as CTO in Sustainable Architecture (2017-2018), where he gained hands-on experience in technical leadership. In 2025, he got his Habit Doctor of Computer Science from the University of Stuttgart. His main research focuses on the art and science of AI planning systems and applied. His broader interests include automated service composition, intelligent energy systems, and learning algorithms from data.
