Studentische Arbeiten

The following projects can be adapted for Diploma Theses, Master Theses, Bachelor Theses, Research Projects (Studienarbeiten) and Student Reports (Fachstudien). Contact Prof. Dr. Marco Aiello in case you have further questions or would like to submit your own proposals.

For Bachelor and Master thesis projects, please follow these instructions:

1. Use the registration form in Campus (including the achieved credit points)
2. Fill in the contract and the licence agreement on-line including your private email in the license agreement. 
3. Bring signed (original) contract, licence agreement, announcement, and the registration to Ms. Ibach
4. Bring the registration form to the Examination office in order to register the thesis and one copy to Ms. Ibach for archiving
5. Please notice that there is a hard deadline of 6 months from the date of registration. The date of registration cannot be on a weekend or an official German holiday.

Note: Concepts and implementation produced in any project must be compatible and published under the MIT license.

The advent of practical Quantum Computing promises to speed up many computation processes, especially in the field of optimization. This means that some large scale simulations and optimizations that were unfeasible with traditional von Neumann architectures become practically available. The proposed research topic focuses on optimisations in the fields of energy distribution under modern models of Smart Grids [2]. The project is open ended. Possible goals include: feasibility of using quantum computing for energy optimization [1], encoding MILP energy optimisations as quantum problems, energy use cases implemented on quantum computers. The interested student can also propose his/her own topic within the sub-field of Quantum Computing for Smart Energy Optimizations.

Keywords: Quantum Computing, Smart Energy Systems, Energy Distribution, MILP

Literature:

[1] Ajagekar, Akshay, and Fengqi You. “Quantum Computing for Energy Systems Optimization: Challenges and Opportunities.” Energy 179 (2019): 76–89. Crossref. Web.

[2] M. Aiello and G.A. Pagani (2016) How energy distribution will change: an ICT perspective, In Smart Grids from a Global Perspective, J. de Wilde, A. Beaulieu, and J. Scherpen (eds), Springer.

Contact: Prof. Dr. Marco Aiello / Daniel Vietz, M. Sc.

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

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.

Contact: Prof. Dr. Marco Aiello

Personal Assistants like Siri, Viv, Alex, Cortana, rely on online services to satisfy the requests of a user. Most often a request cannot be satisfied with just one service, but it needs the composition of multiple services. In this project you will investigate techniques to intelligently compose services  and you will implement prototypes connecting to Web services to satisfy complex user requests. 

Literature

Kaldeli, A. Lazovik, and M. Aiello (2016) Domain-Independent Planning for Services in Uncertain and Dynamic Environments, Artificial Intelligence, Elsevier, 236(7): 30-64.

Contact: Prof. Dr. Marco Aiello

Today researchers heavily rely on research grants and subsidies to finance their research. The attribution of such subsidies is usually based on a peer-evaluation process that awards grants to the most promising proposals. In this project you will perform a statistical analysis of grant acquisition. You will provide a model to predict grant distribution and simulate various scenarios to identify the effects of specific policies on the probabilistic distribution of grants and on their impact on research and innovation. Bibliometric data for complex network analysis will be part of the developed model.

Contact: 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 program and its 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.

Contact: Prof. Dr. Marco Aiello

Cellular automata are simple models that can represent complex even chaotic systems. The goal of this project is to provide simple models for resource allocation that help identifying efficient allocation rules. The project entails both a modelling and simulation component to it.

Contact: Prof. Dr. Marco Aiello

Model Checking is the field concerned with the automatic process of checking whether a given model (or family of models) meets certain specifications. Model checkers work in various ways usually exploring large models explicitly represented as states or symbolically represented. Your task is to evaluate the impact of using complex network analysis techniques to improve and extend model checking approaches.

Contact: Prof. Dr. Marco Aiello

Artificial Intelligence (AI) planning provides powerful techniques for searching and selecting actions that, when executed, achieve a given goal [1]. Finding solutions to planning problems is challenging: planning problems are often NP-hard or harder. Planning problems are currently addressed by either finding valid solutions using classical algorithms and selecting the appropriate ones (e.g., optimal) by hand or finding "good" solutions using heuristics. Even though these two approaches do not guarantee the generation of optimal solutions, both require substantial processing power.

The emergence of quantum computing promises an exploration of hard computational problems that are considered to be too demanding for classical computers [2, 3]. The hypothesis is that existing AI planning techniques could be enhanced by quantum algorithms that improve such techniques in the efficiency of finding suitable (e.g., optimal) plans, in the finding better plans (e.g., satisfy more constraints), and/or in a greater diversity of the plans found [4]. The possible goals of this project include exploring the feasibility of enhancing existing approaches on Hierarchical Task Network (HTN) planning [5] with quantum algorithms, encoding HTN planning problems as quantum problems, and demonstrating the benefits of quantum HTN planning on use cases. Interested students can also propose their own topics within the scope of the project.

Keywords: HTN planning, quantum computing

Literature

[1] M. Ghallab, D. S. Nau, and P. Traverso, Automated planning: Theory & practice. Morgan Kaufmann Publishers Inc., 2004.

[2] VV. N. Smelyanskiy, E. G. Rieffel, S. I. Knysh, C. P. Williams, M. W. Johnson, M. C. Thom, W. G. Macready, and K. L. Pudenz, "A near-term quantum computing approach for hard computational problems in space exploration", arXiv: Quantum Physics, 2012.

[3] R. Desimone, P. Warburton, Y.-L. Fang, A. Montanaro, S. Piddock, S. Yarkoni, A. Mason, C. White, and T. Popa, "Quantum computing algorithms for optimised planning and scheduling applications (QCAPS)", Plantagenet Systems Ltd, QCAPS/133087/Final Report/D008/v1.0, 2018.

[4] R. Biswas, Z. Jiang, K. Kechezhi, S. Knysh, S. Mandra, B. O'Gorman, A. Perdomo-Ortiz, A. Petukhov, J. Realpe-Goomez, E. Rieffel, D. Venturelli, F. Vasko, and Z. Wang, "A NASA perspective on quantum computing: Opportunities and challenges", Parallel Comput., 64, 81-98, 2017.

[5] I. Georgievski and M. Aiello, "HTN planning: Overview, comparison, and beyond", Artif. Intell., vol. 222, pp. 124-156, 2015.

Contact: Dr. Ilche Georgievski

Commercial buildings are characterised by high operational costs and high CO₂ emission levels due to the high demand for energy [1]. The management of energy demand, also known as demand-side management, allows buildings to make informed decisions about their energy consumption, but it also helps energy providers to shape load demand profiles [2]. However, current building management systems are capable of controlling heating, ventilation and air conditioning, lighting and other aspects using specific strategies limited by basic scheduling functions [3]. On the other hand, with the proliferation of the Internet of Things (IoT) deployed in building, building management systems can be transformed into systems employing intelligent demand-side management. In other words, buildings equipped with various sensors and actuators have the potential to become intelligent and provide occupants with a more comfortable experience, reduced operational costs, and improved energy efficiency.

This calls for developing approaches that can plan the energy demand of buildings intelligently. In this context, Artificial Intelligence (AI) planning offers powerful techniques for automated and dynamic selection and organisation of actions that, when executed, achieve a given objective [4]. AI planning could be helpful for buildings to intelligently plan their demand by computing effective plans or schedules of device actions.

The aim of this project is to propose and develop an approach for demand-side management via AI planning. This involves defining the problem of demand-side management as a domain-independent planning problem, modelling the planning problem using a standard syntax, such as PDDL [5], solving the planning problem using existing AI planners, and evaluating the efficiency and effectiveness of the proposed approach.

Keywords: AI planning, demand-side management, Internet of Things, office buildings

Literature

[1] D. D'Agostino, B. Cuniberti, and P. Bertoldi, Energy consumption and efficiency technology measures in European non-residential buildings," Energy and Buildings, vol. 153, pp. 2-86,2017.

[2] P. Palensky and D. Dietrich, Demand side management: Demand response, intelligent energy systems, and smart loads," IEEE Transactions on Industrial Informatics, vol. 7, no. 3, pp. 381-388, 2011.

[3] I. Georgievski, T. A. Nguyen, F. Nizamic, B. Setz, A. Lazovik, and M. Aiello, Planning meets activity recognition: Service coordination for intelligent buildings," Pervasive and Mobile Computing, vol. 38, no. 1, pp. 110-139, 2017.

[4] M. Ghallab, D. S. Nau, and P. Traverso, Automated planning: Theory & practice. Morgan Kaufmann Publishers Inc., 2004.

[5] M. Fox and D. Long, PDDL2.1: An extension to PDDL for expressing temporal planning domains," Journal of Artificial Intelligence Research, vol. 20, no. 1, pp. 61-124, 2003.

Contact: Dr. Ilche Georgievski

Ubiquitous computing aims at creating ambience in which one's experiences and quality of life are improved by monitoring and assisting people using Internet of Things and intelligence in coherence. Artificial Intelligence (AI) planning provides a sophisticated form of intelligence [1] and appears to be highly suitable for ubiquitous computing [2]. Even though AI planning has been used to solve problems in many planning domains in ubiquitous computing, it is still unknown how difficult such problems are and the amount of resources those problems require.

Knowing the complexity of planning in specific domains gives an opportunity to outline the speed and length of plans generated by AI planners. Since it is impossible to analyse the complexity of every planning domain in ubiquitous computing, we can look at the domains from a broader perspective - a single and general ubiquitous computing planning domain (benchmark domain) [3]. Benchmarking should help answer how AI planners perform on such a benchmark domain, how their performance compare on other benchmark domains, what are the main reasons for differences in performance, and what could be learned from other domains to potentially improve the ubiquitous computing planning domain [4]?

The aim of this project is to define and model a benchmark planning domain for ubiquitous computing. This involves defining a general ubiquitous computing planning domain and three specialisations of it (e.g., non-temporal, temporal, offices, houses, etc.), deciding which tasks, actions, and objects should be part of the general domain, modelling the domain in PDDL, and possibly testing a selection of AI planners on the PDDL version of the benchmark domain.

Keywords: Ubiquitous computing, planning benchmarks, planning domains

Literature

[1] M. Ghallab, D. S. Nau, and P. Traverso, Automated planning: Theory & practice. Morgan Kaufmann Publishers Inc., 2004.

[2] I. Georgievski, "Coordinating services embedded everywhere via hierarchical planning", Ph.D. dissertation, University of Groningen, October 2015.

[3] I. Georgievski and M. Aiello, "Automated planning for ubiquitous computing", ACM Comput. Surv., vol. 49, no. 4, pp. 63:1-63:46, 2016.

[4] J. Hoffmann, S. Edelkamp, S. Thiebaux, R. Englert, F. dos Santos Liporace, and S. Trüg, "Engineering benchmarks for planning: The domains used in the deterministic part of ipc-4" J. Artif. Int. Res., vol. 26, no. 1, pp. 453-541, 2006.

Contact: Dr. Ilche Georgievski

Modern software applications are increasingly deployed and distributed on infrastructures in the Cloud. Before the deployment process happens, these applications are being manually - or with some predefined scripts - composed from various smaller interdependent components. With the increase in demand for, and complexity of applications, the composition process becomes an arduous task often associated with errors and a suboptimal use of computer resources. Dynamic reconfiguration, which is the process of reconfiguring an application while it is running, only aggravates the problem even further [1]. Such processes can be alleviated by automatically and dynamically composing and reconfiguring Cloud applications , especially when clear semantics exists that describe the deployment and reconfiguration problems, such as the Aeolus formal model [2].

Artificial Intelligence (AI) planning provides techniques for searching and selecting actions that, when executed, achieve a given goal [3]. In the context of the Cloud, AI planning can provides powerful methods for searching in large and complex Cloud infrastructures to find "good" compositions and reconfigurations of Cloud applications. Additionally, AI planning can be used to handle the Cloud uncertainty (e.g., failures of hardware resources), find deployments optimal with respect to the use of computer resources, etc. Among AI planning techniques, Hierarchical Task Network (HTN) planning appears suitable for solving deployment problems [4].

The aim of this project is to study and solve both deployment and reconfiguration problems using HTN planning. The project should build upon [4]. The project tasks include:

• Refine the definition of the Aelous-based deployment problem as an HTN planning problem (to improve performance);
• Define the Aelous-based reconfiguration problem as an HTN planning problem;
• Model such HTN planning problems using HPDL and/or SHOP2;
• Evaluate the approach using one or more existing AI planners;
• Compare the performance of existing AI planners among themselves and with Metis [5].

Keywords: Automated deployment, dynamic reconfiguration, cloud applications, HTN planning

Literature

[1] N. Arshad, D. Heimbigner, and A. Wolf, "Deployment and dynamic reconfiguration planning for distributed software systems," in International Conference on Tools with Artificial Intelligence, ser. ICTAI'03, 2003, pp. 39-46.

[2] R. Di Cosmo, J. Mauro, S. Zacchiroli, and G. Zavattaro, "Aeolus: A component model for the cloud," Information and Computation, vol. 239, pp. 100-121, 2014.

[3] M. Ghallab, D. S. Nau, and P. Traverso, Automated planning: Theory & practice. Morgan Kaufmann Publishers Inc., 2004.

[4] I. Georgievski, F. Nizamic, A. Lazovik, and M. Aiello, "Cloud ready applications composed via htn planning," in IEEE International Conference on Service Oriented Computing and Applications, ser. SOCA'17, 2017, pp. 23-33.

[5] T. A. Lascu, J. Mauro, and G. Zavattaro, "A planning tool supporting the deployment of cloud applications," in International Conference on Tools with Artificial Intelligence, ser. ICTAI'13, 2013, pp. 213-220.

Contact: Dr. Ilche Georgievski

Commercial buildings, such as office buildings, are characterised by high operational costs, high CO₂ emission levels due to the high energy demand, and limited comfortability [1]. Current building management systems control building operations in terms of heating, ventilation, air conditioning, and lighting using specific strategies limited by basic scheduling functions [2]. On the other hand, with the proliferation of the Internet of Things (IoT) deployed in buildings, such systems can be transformed into entities capable of intelligent decision-making. In other words, buildings equipped with various sensors and actuators have the potential to become intelligent and provide occupants with an improved experience in terms of comfort and productivity, reduced operational costs, and improved energy efficiency.

This calls for employing approaches that can plan the energy demand of buildings intelligently. In this context, Artificial Intelligence (AI) planning offers powerful techniques for automated and dynamic selection and organisation of actions that, when executed, achieve a given objective [3]. AI planning could be helpful for buildings to intelligently plan their operation by computing effective plans or schedules of device actions, but also to assist their occupants by computing plans of user actions that can improve their comfort and productivity. To compute such plans of action, AI planning requires a planning domain model in which actions need to be specified in terms of preconditions that need to hold so that an action can be applied and postconditions as effects that the action has after its application. Planning domains are typically authored manually by domain experts, which is an arduous and error-prone process. Alternatively, such planning domains can be automatically generated using machine learning techniques [4].

The aim of this project is to develop a system that can learn planning domain models for office buildings using IoT data. The main project tasks include:

• Examine the current state of the art on learning planning domains for AI planning with an emphasis on planning domains for commercial buildings.
• Develop an approach for learning planning domains from IoT data. This requires analysis and selection of appropriate machine learning techniques.
• Use existing IoT datasets and/or deploy IoT in an environment to gather the necessary data.
• Learn planning domains specified in the Planning Domain Definition Language (PDDL) [5].
• Evaluate the approach in terms of effectiveness of learning PDDL planning domains. Also, evaluate the suitability of the learned PDDL planning domain(s) when used by existing planners, such as FF [6] and FD [7], to compute plans.

Keywords: AI planning, machine learning, Internet of Things, office buildings

Literature

[1] D. D'Agostino, B. Cuniberti, and P. Bertoldi, "Energy consumption and efficiency technology measures in european non-residential buildings", Energy and Buildings, vol. 153, pp. 2-86, 2017.

[2] I. Georgievski, T. A. Nguyen, F. Nizamic, B. Setz, A. Lazovik, and M. Aiello, "Planning meets activity recognition: Service coordination for intelligent buildings", Pervasive and Mobile Computing, vol. 38, no. 1, pp. 110-139, 2017.

[3] M. Ghallab, D. S. Nau, and P. Traverso, Automated planning: Theory & practice. Morgan Kaufmann Publishers Inc., 2004.

[4] S. Jimenez, T. De La Rosa, S. Fernandez, F. Fernandez, and D. Borrajo, "A review of machine learning for automated planning", The Knowledge Engineering Review, vol. 27, no. 4, pp. 433-467, 2012.

[5] M. Fox and D. Long, "PDDL2.1: An extension to PDDL for expressing temporal planning domains", Journal of Artificial Intelligence Research, vol. 20, no. 1, pp. 61-124, 2003.

[6] J. Hoffmann. (1997) Fast forward homepage. [Online]. Available: https://fai.cs.uni-saarland.de/hoffmann/ff.html

[7] F. Downward. (2003) Fast downward homepage. [Online]. Available: http://www.fast-downward.org

Contact: Dr. Ilche Georgievski

Artificial Intelligence (AI) planning provides powerful techniques for searching and selecting actions that, when executed, achieve a given goal [1]. The field of AI planning is a long-standing one and the amount of research is so big and increasing rapidly that understanding exactly how far the field has progressed has become more difficult. On the other hand, the interest and scope for exploitation of AI planning in industry and commerce are continuously increasing. For example, automation is a trend that requires efficient and robust AI planning. So, it is significant for both practitioners and scientists to understand the current state of the field of AI planning and especially the emerging research topics among the numerous publications. Since providing a systematic overview of the field manually by experts is arduous and time-consuming, a computation approach seems more appropriate. Complex network analysis offers quantitative techniques for studying the structures and characteristics of networked systems [2]. Citation network analysis is especially relevant because it focuses on exploring the frequency and patterns of citations in documents, including academic publications [3]. It has been used for the analysis of various fields, such as learning analytics [4], regenerative medicine [5], organic photovoltaic cells [6], human-resource development [7], and others.

The aim of this project to evaluate the current state of the field of AI planning by (citation) network analysis. The focus is on articles appearing in the ICAPS, IJCAI and AAAI conferences. The questions of interest are:

• What structural attributes and characteristics are present in the network(s) of AI planning? Are there any specificities in knowledge flows between countries, institutions, and technology fields in the field of AI planning?
• What are the main interests, topics, and issues of AI planning? Which trends are emerging? Which disciplinary hierarchies have been influencing the development of the field so far? Which are under-represented areas of research? Which disciplines may require more strategic and targeted support and funding opportunities? Are cited papers of a more conceptual or empirical nature?
• Which authors and publications are most influential to the field of AI planning, and to which specific areas of automated planning do they contribute knowledge?
• Are there distinctive subgroups in the network(s) of AI planning? If yes, what are they? Are there in-group citations?

Keywords: complex network analysis, citation network, AI planning

Literature

[1] M. Ghallab, D. S. Nau, and P. Traverso, Automated planning: Theory & practice. Morgan Kaufmann Publishers Inc., 2004.

[2] M. Newman, Networks: An Introduction. USA: Oxford University Press, Inc., 2010.

[3] H. Small, Tracking and predicting growth areas in science," Scientometrics, vol. 68, no. 3, pp. 595-610, 2006.

[4] S. Dawson, D. Gasevic, G. Siemens, and S. Joksimovic, Current state and future trends: A citation network analysis of the learning analytics field," in International Conference on Learning Analytics And Knowledge, ser. LAK '14, 2014, pp. 231-240.

[5] N. Shibata, Y. Kajikawa, Y. Takeda, I. Sakata, and K. Matsushima, Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications," Technological Forecasting and Social Change, vol. 78, no. 2, pp. 274-282, 2011.

[6] H. Choe, D. H. Lee, I. W. Seo, and H. D. Kim, Patent citation network analysis for the domain of organic photovoltaic cells: Country, institution, and technology field," Renewable and Sustainable Energy Reviews, vol. 26, no. 2013, pp. 492-505, 2013.

[7] S. J. Jo, C.-W. Jeung, S. Park, and H. J. Yoon, Who is citing whom: Citation network analysis among HRD publications from 1990 to 2007," Human Resource Development Quarterly, vol. 20, no. 4, pp. 503-537, 2009.

Contact: Dr. Ilche Georgievski