Verfügbare Projektthemen

On this page, we provide the current project topics we offer. The project topics can be adapted for Diploma Theses, Master Theses, Bachelor Theses, Research Projects, Student Reports (Fachstudien), and Study Projects. It is also possible to submit your own proposal for a project.

Interested in our project topics or you have your own proposal? Please send your inquiry to scsp@iaas.uni-stuttgart.de.

When sending the inquiry, please consider the following:

• You are a student enrolled in a study program offered by or associated with the Computer Science department.
• The inquiry includes one or more of the topics below.
• In the case of your own proposal, describe the topic and its relevance to our research interests.

We receive many inquiries, so we ask for your patience in waiting for an answer. If you do not hear from us within two weeks, please send a reminder to the same email address.

We are looking for a student interested in technology transfer for which funds are available. Please contact Prof. Aiello if you are interested in exploring this opportunity.

Gear failure caused by pitting is one of the leading reasons of downtime of gear units in industrial plants and systems. As soon as a pitting exceeds a size of 4 % in relation to the size of the tooth flank, the gear is considered as failed. An adaptive operating strategy applies a load reduction of a damaged tooth by the means of torque variation to increase the Remaining Useful Life. A usage scenario for such an adaptive operating strategy can be, e.g., the gears of wind power drives to make them last longer and reduce the need for repairs or replacements.
In cooperation with the Institute of Machine Elements, we offer the opportunity to develop a holistic operating strategy to reduce gear wearing. The objective of this work is, therefore, to design and implement a software solution that realizes the adaptive operating strategy by evaluating sensor data and proposing correcting operating modes, e.g., by using machine learning, control theory, or any other means suitable for self-adaptive systems. The resulting system can then be evaluated using real hardware on our test bench.

Keywords: Self-adaptive Systems, Operating Strategies, Gears, Wind Power Drives, Sustainability

Literature:

[1] D. Weyns, An introduction to self-adaptive systems - A comtemporary software engineering perspective, ser. Wiley - IEEE. Hoboken, NJ: Wiley-Blackwell, Oct. 2020. [Online]. Available: http://dx.doi.org/10.1002/9781119574910

[2] Y. Gretzinger, “Steigerung der nutzbaren Restlebensdauer von Zahnrädern durch
eine adaptive Betriebsstrategie,” 2022. [Online]. Available: http://elib.uni-stuttgart.de/handle/11682/12162

Supervisors: Robin Pesl, M.Sc. / Lisa Binanzer, M. Sc.

In 1992, Nagel and Schreckenberg presented a cellular model of traffic that with just four rules describes quite well highway traffic behavior. In the proposed project, the idea is to research a similar model for motorcycle platooning. Starting from maps of winding roads of the black forest, the goal is to build a cellular automata model of a platoon of motorcycles driving through those roads; i.e., to model the behaviour of a group of motorcyclists that are driving together without passing each other. 

Possible metrics for evaluation are the acceleration/deceleration of vehicles depending on position in the platoon, how fun a road is based on the patter of curves and the size of the platoon and how often it is necessary to brake beacuse of a motorcycle in front, comparison with original car traffic model.

Literature:

[1] Nagel, K.; Schreckenberg, M. (1992). "A cellular automaton model for freeway traffic" (PDF). Journal de Physique I. 2 (12): 2221. Bibcode:1992JPhy1...2.2221N. doi:10.1051/jp1:1992277. Archived from the original (PDF) on 2014-03-11.

[2] Meng, J. P., Dai, S. Q., Dong, L. Y., & Zhang, J. F. (2007). Cellular automaton model for mixed traffic flow with motorcycles. Physica A: Statistical Mechanics and Its Applications, 380, 470-480.

[3] Lan, L. W., Chiou, Y. C., Lin, Z. S., & Hsu, C. C. (2010). Cellular automaton simulations for mixed traffic with erratic motorcycles’ behaviours. Physica A: Statistical Mechanics and its Applications, 389(10), 2077-2089.

[4] http://modelingcommons.org/browse/one_model/4806#model_tabs_browse_nlw

Supervisors: Prof. Dr. Marco Aiello 

Electric Vehicles (eV) have a battery size that can vary from few KWh to almost 100 KWh. These are frequently connected to the power grid to charge. Several researchers have proposed the idea that these batteries can also be used to store grid electricity and provided it back to grid during peak demand periods. The research proposed here is to assess technical solutions to make the eV an integral part of the power grid, to study individual strategies based on dynamic pricing for incentivising eV integration, and to assess the economic viability of such approaches.

Literature

P. P. Malya, L. Fiorini, M. Rouhani, and M. Aiello (2021) Electric Vehicles as Distribution Grid Batteries: A Reality Check, Energy Informatics, Springer, 4(Suppl 2):2.

A. P. Lopes, F. J. Soares and P. M. R. Almeida, "Integration of Electric Vehicles in the Electric Power System," in Proceedings of the IEEE, vol. 99, no. 1, pp. 168-183, Jan. 2011.

Francis Mwasilu, Jackson John Justo, Eun-Kyung Kim, Ton Duc Do, Jin-Woo Jung, “Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration,” in Renewable and Sustainable Energy Reviews, vol. 34, no. 6, pp 501-516, Jun. 2014.

Supervisor: Prof. Dr. Marco Aiello

Software running on data centres and personal computers is responsible for a large amount of energy consumption and consequent emissions worldwide. At the same time, it is very difficult to perform an accurate measure of energy consumption for individual software programs and their individual components, this is also due to the current software engineering practices of layering and virtualisation. For this research project you are asked to look into energy models for software, energy accountability for software, comparing the running of the same algorithm on different platforms for measuring the energy consumption, etc.

Literature

[1] S. Miller and C. Lerche (2022) Sustainability with Rust. AWS Open Source Blog. Online: https://aws.amazon.com/de/blogs/opensource/sustainability-with-rust/. 

[2] Green Software Lab (2018) Energy Efficiency in Programming Languages (source code of 10 benchmarks implemented in 28 programming languages). GitHub. Online: https://github.com/greensoftwarelab/Energy-Languages. 

Supervisor: Prof. Dr. Marco Aiello, Dr. Ilche Georgievski

The goal is to investigate the capabilities of Large Language Models (LLMs) in generating and correcting planning domain models and executing complex cognitive tasks, specifically focusing on planning and reasoning. Although traditionally used for language processing based on probabilistic retrieval methods, LLMs such as GPT-3 have shown unexpected linguistic behaviors, raising questions about their potential in more structured cognitive domains. The project will methodically evaluate LLM's performance in generating plans for a series of planning tasks derived from domains (e.g., those of the International Planning Competition) and in generating and correcting domain models.

Literature:

Kambhampati, Subbarao, et al. "LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks." arXiv preprint arXiv:2402.01817 (2024).

Kambhampati, S. (2024). Can large language models reason and plan?. Annals of the New York Academy of Sciences.

Huang, W., Abbeel, P., Pathak, D., & Mordatch, I. (2022, June). Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. In International Conference on Machine Learning (pp. 9118-9147). PMLR.

Pallagani, V., Muppasani, B., Murugesan, K., Rossi, F., Srivastava, B., Horesh, L., ... & Loreggia, A. (2023). Understanding the capabilities of large language models for automated planning. arXiv preprint arXiv:2305.16151.

Supervisors: Prof. Dr. Marco Aiello / Dr. Ilche Georgievski / Ebaa Alnazer, M.Sc.

Explore how Large Language Models (LLMs), like GPT-4, can revolutionize automated service composition. This project focuses on the integration of LLMs into the service composition pipeline, transforming how adaptive information systems are constructed from loosely-coupled network services. The research is about delving into novel methods for unsupervised service composition, orchestration, and testing. Possible projects involve:

• Comparing LLMs for the task of service discovery, service composition, service orchestration
• Composition code generation with LLMs and their deployment
• Composition testing and validation

Literature:

Aiello, M., & Georgievski, I. (2023). Service composition in the ChatGPT era. Service Oriented Computing and Applications, 17(4), 233-238.

Pesl, R. D., Stötzner, M., Georgievski, I., & Aiello, M. (2023, November). Uncovering LLMs for Service-Composition: Challenges and Opportunities. In International Conference on Service-Oriented Computing (pp. 39-48). Singapore: Springer Nature Singapore.

Wolfram, S.: What Is ChatGPT Doing ... and Why Does It Work? Wolfram Research, Incorporated, Champaign, Illinois (2023)Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M.T., Zhang, Y.: Sparks of Artificial General Intelligence: Early experiments with GPT- 4. Technical Report 2303.12712, arXiv (2023)

Meyer, B.: What do ChatGPT and AI-based automatic program generation mean for the future of software. BLOG@Communications of the ACM 12 (2022)

Supervisors: Prof. Dr. Marco Aiello / Dr. Ilche Georgievski / Robin Pesl, M.Sc.

With the increment of CO2 emission, the world energy consumption, and the diversity in energy sources (finite and renewable), the goals of building management systems are widened to include cutting energy consumption; hence preserving finite resources, using non-carbon sources when possible to lower the CO2 footprint, and lowering costs for consumers and businesses while preserving users comfort [2]. This is especially true for buildings that are connected to a smart grid, which makes it possible to include renewable sources and provides dynamic pricing and energy offers coming from competing providers [4]. The advent of the Internet of Things (IoT) offers significant opportunities to improve the limited control capabilities offered by current building management systems [5]. In other words, equipping commercial buildings with different sensors and actuators allows management systems to make informed and "smart" decisions to control the environment and adjust it towards satisfying predefined objectives, such as energy saving. This "smartness" implies automating the decision-making process. Artificial Intelligence (AI) planning fits perfectly for this purpose since it offers automation techniques to intelligently search and compose a sequence of actions, i.e., plans when executed, can achieve a predefined set of goals [6].

Planning in such domains is challenging due to several types of uncertainty, e.g., weather forecasting, demand forecasting, and market prices forecasting [3, 2]. Consider the following example. A smart building is connected to a smart grid and has the goal of maintaining economical and environmental sustainability. The building can get its electricity from two different sources; either from stored electricity generated locally by renewable resources or from electric utilities with varying prices and energy offers. The choice of whether to consume the locally generated electricity or the purchasing it from outside sources should be made under uncertainty about the future market prices and the future weather forecast. In many approaches, AI planning problems are modelled with uncertainties about the action outcomes in mind; hence the resulting plans in such approaches are of a probabilistic nature [7, 1].

The aim of this project is to develop an AI planning-based approach in such domains. This involves defining the entities that are part of these domains, defining the types of uncertainty that can arise in these domains, modelling the AI planning problem by using a standard syntax that can model some sort of uncertainty, e.g., PPDDL [8], solving the planning problem using existing AI planners that can deal with uncertainties, and evaluating the efficiency and effectiveness of the proposed approach.

Keywords: sustainable buildings, Internet of Things, AI planning

Literature

[1] Blythe, J.: An overview of planning under uncertainty. Artificial Intelligence Today, pp. 85-110 (1999).

[2] Fiorini, L., Aiello, M.: Energy management for user’s thermal and power needs: A survey. Energy Reports 5, 1048-1076 (2019).

[3] Fiorini, L., Aiello, M.: Predictive multi-objective scheduling with dynamic prices and marginal CO2-emission intensities. In: Proceedings of the Eleventh ACM International Conference on Future Energy Systems. pp. 196-207 (2020).

[4] Georgievski, I.: Coordinating services embedded everywhere via hierarchical planning. Ph.D. thesis, University of Groningen (2015).

[5] Georgievski, I., Nguyen, T.A., Nizamic, F., Setz, B., Lazovik, A., Aiello, M.: Planning meets activity recognition: Service coordination for intelligent buildings. Pervasive and Mobile Computing 38, 110-139 (2017).

[6] Ghallab, M., Nau, D., Traverso, P.: Automated Planning: theory and practice. Elsevier (2004).

[7] Koenig, S., Simmons, R.G.: Risk-sensitive planning with probabilistic decision graphs. In: Principles of Knowledge Representation and Reasoning. pp. 363-373. Elsevier (1994).

[8] Younes, H.L., Littman, M.L.: PPDDL1. 0: An extension to PDDL for expressing planning domains with probabilistic effects. Techn. Rep. CMU-CS-04-162 2, 99 (2004).

Supervisor: Ebaa Alnazer, M.Sc.