TOPIC 1: Current Trends in Deep Reinforcement Learning (DRL)
Introduction: Merging methods from Reinforcement Learning and Deep Learning has proven in recent years to result in a powerful approach for solving previously unsolvable problems, for example in computer vision, robotics, or multi-agent coordination.
As with many advancements in AI, there is a vast variety of research articles exploring the applications of DRL, as well as potential refinements and connections with other fields. Therefore, selecting the right approach for a given problem requires not only an overview of the ongoing work, but also an understanding of the connections and discrepancies between the different trends and methods.
Goal and Objective: In this seminar, you will gather an overview of the field of Deep Reinforcement Learning, present current approaches together with their respective advantages, drawbacks and main application areas, and particularly explore the potential usage of DRL for cooperative Multi-Agent Systems.
Introduction: Requirements Engineering consists of multiple activities and techniques in order to define, document and maintain software artefacts. Advances in Natural Language Processing (NLP) in the last years have also impacted Requirements Engineering. The goal of this seminar work is to provide a clear overview of approaches that apply various NLP methods to support Requirements Engineering (especially Requirements elicitation) with a focus on current approaches.
Goal and Objective: Overview of different state-of-the-art approaches that apply NLP technologies in the field of Requirements Engineering and comparison between them
TOPIC 3: Microservice architectures - Integration/Composition and Security - Challenges and Solutions
Introduction: In the recent years microservice architectures have gained popularity to build more manageable applications compared to monolithic systems in certain situations. But microservices have their own problems and limitations in terms of complexity and security.
The greater number of independent components in the system makes it harder to compose or integrate the functionality of these components or services to supply an API for the different needs of various specific clients or use cases. Furthermore the greater number of components makes it more difficult to achieve fine-grained authorization.
The goal of this seminar is to clearly identify the problems (and the respective solutions discussed in the literature) associated with microservice architectures in terms of integration/composition and security.
Goal and Objective: Gain an understanding of the core concepts and patterns of microservice architectures in comparison to monolithic architectures. Examine the challenges of microservice architectures especially in terms of composition/integration and security and provide an overview of the available solutions or patterns for these challenges alongside with their advantages and disadvantages.
TOPIC 4: Policy Enforcement Approaches
Introduction: In terms of software development requirements are common, regarding if the behavior of ready to use and running code the conforms to user demands is not as widespread as, but a very important aspect to prevent misuse of data. There are different known approaches of policy enforcement, including static and dynamic approaches and also approaches putting the software component or the runtime environment in charge of enforcement.
Goal and Objective: The objective of this seminar is to give an overview of different approaches to express and enforce policies. This overview should arrange and compare approaches, work out advantages and drawbacks found in the literature.
TOPIC 5: Machine learning techniques for preference learning in pervasive and autonomous systems
Introduction: One of the major goals of pervasive systems is to adapt to user preferences with minimal intervention of the user. Over the past decades several approaches have utilized different machine learning approaches for preference learning. The kind of machine learning approaches used, depend on the focus of the preference learning approach that utilize them. Some approaches focus on the observation of user behavior, whereas other approaches learn preferences on the basis of observed context states.
Goal and Objective: In this seminar you will research and compare several approaches for preference learning in the context of pervasive and autonomous systems. Another important aspect of the seminar is to name the advantages and disadvantages of the approaches as well as highlighting which machine learning techniques are commonly used.
|Institute for Enterprise Systems||L15, firstname.lastname@example.org||Office Management|
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