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Some topics for BA and MA theses
Methods for artificial neural network explicability  
A current challange in Artificial Intelligence and Machine Learning is to find methodologies on how to decode representations of problems within the weightspace of artificial neural networks (ANNs). while some approaches focus on artificial neural networks trained on visual data to enhance interpretability of activity through relevance propagation, others specify formal models for aquiring insight into semantic representations in network topology. Within the interdisciplinary scope between Neuroscience, Artificial Intelligence and mathematics, students are encouraged to pick from the following Thesis topics on ANN explicability:

Analyzing Twitter Discussions with Weighted Bipolar Argumentation Frameworks  
Recently, computational argumentation has been investigated as a tool for analyzing discussions on social media platforms. For instance, a Twitter discussion can be modeled as a graph where each node denotes a tweet and an edge from tweet t1 to tweet t2 indicates that t1 replies to t2. Sentiment analysis tools can be applied to classify edges as attacks (tweets criticizing other tweets), supports (tweets supporting other tweets) or other (currently irrelevant relations). A social relevance score can be assigned to tweets based on statistical data like the number of followers of the author or the number of retweets. Given this graph with social relevance scores for each tweet and attack and support relations between tweets, we are interested in assigning a strength value between 0 and 1 to each position. 0 means that the position should be rejected and 1 means that it should be accepted. A value of 0.5 expresses indifference, whereas values close to 0 (1) express that we tend to reject (accept) the position. If there are no attackers and supporters, the strength should be just the social relevance score. If attackers and supporters are present, the strength must be adapted based on the strength of attackers and supporters. During the last years, some weighted bipolar argumentation frameworks have been presented that can compute such strength values. In this thesis, weighted bipolar argumentation frameworks shall be evaluated for the analysis of Twitter discussions. To this end, you should build up on existing work in this field. In [1], a related framework has been presented that can only accept or reject arguments (no graded acceptance values between 0 and 1) and considers only attack relations (no support). However, the ideas to compute social relevance scores and to identify attacking tweets using sentiment analysis tools will also be helpful for this work. The frameworks that should be considered in this thesis are the DFQuAD algorithm from [2], the Eulerbased restricted semantics from [3] and another currently unpublished framework that has been developed in our group. In order to work on this thesis successfully, you should have basic knowledge of machine learning and artificial intelligence in general (Introduction to AI and Methods of AI will be sufficient), should be comfortable with formal approaches and have proficient programming skills in Java or Python. [1] Alsinet, T., Argelich, J., Béjar, R., Planes, J., Cemeli, J., & Sanahuja, C. 2017. A Distributed Approach for the Analysis of Discussions in Twitter. In Proceedings of the 3rd International Workshop on Social Influence Analysis (SocInf 2017), 4556. [2] Rago, A.; Toni, F.; Aurisicchio, M.; and Baroni, P. 2016. Discontinuityfree decision support with quantitative argumentation debates. In International Conference on Principles of Knowledge Representation and Reasoning (KR), 63–73. [3] Amgoud, L., and BenNaim, J. 2017. Evaluation of arguments in weighted bipolar graphs. In European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, 25–35. Contact: Nico Potyka 
Computational Storytelling using MultiAgent Simulation  
To model computational creativity in the domain of stories, one first needs to develop a computational representation for these multifaceted and dynamic works of ingenuity. We approach this by representing a narrative's storyworld and characters as a multiagent simulation system. The plot of a story, in this case, emerges from the (inter) actions of the system's agents in combination with the internal events in the agents' reasoning cycles: the plans, beliefs, desires and emotions that make them act and react in certain ways.

Conceptual Spaces for Artificial Intelligence  
The cognitively inspired framework of conceptual spaces proposes to encode knowledge based on geometric representations: Objects are mapped onto points in a semantic feature space and concepts are mapped onto convex regions in this space. This approach has various advantages:
In the context of our current research on conceptual spaces for AI, we have already developed and implemented a thorough mathematical formalization of the framework. The following potential thesis topics explore applications and extensions of this formalization:
[All topics are available as both bachelor and master thesis.] Contact: Lucas Bechberger 
(Deep) Learning in the Health Domain  
Through a cooperation with a health consulting company, we have access to a large data set of health data including both structured information (hierarchical codes for diagnosed illnesses and applied treatments) and unstructured information (laboratory values for blood samples). This data set is well suited for exploratory studies on using machine learning in the health domain to predict probable diagnoses, how long the patient will stay in the hospital, what kind of treatment is appropriate, etc.  
Dialog Systems for Cognitive Robotics  
Currently ongoing work focuses on the usage of a Pepper robot as a tutor in a classroom setting. In order to allow for an interactive dialog with students, we are interested in developing a dialog system in this cognitive robotics context.  
Learning and User Modeling for ELearning Platforms  
 
Heuristics for theory projection in analogical reasoning  
HeuristicDriven Theory Projection (HDTP) is a framework for analogical reasoning currently under development at the IKW. It works in two stages by first identifying common structures in the modelling of two different domains and then transferring knowledge und proposing new conclusions.  
The semantics of theory projection  
HeuristicDriven Theory Projection (HDTP) is based on an extended form of antiunification of logical theories. It introduces certain secondorder elements which seem to be wellfounded on a syntactic level but whose semantics needs to be further investigated. A thesis should develop a model theoretic semantics and show its applicability in the description of analogies.  
Comparing analogy models  
HeuristicDriven Theory Projection (HDTP) is a framework for analogical reasoning currently under development at the IKW. The representation is essentially logicbased. HDTP works in two stages by first identifying common structures in the modelling of two different domains and then transferring knowledge und proposing new conclusions.  
Analogical reasoning and learning  
Analogy making is the process by which humans extract operators used to solve one problem and map them onto a solution for another problem. The thesis shall investigate experimentally the human way of analogical reasoning and learning.  
Learning firstorder logical theories with neural networks in reasoning processes  
The wellknown gap in Cognitive Science and Artificial Intelligence between symbolic and subsymbolic modelings seems to be a hard problem. An approach developed at the IKW to bridge this gap in one direction uses a translation of logical theories to a semisymbolic representation, namely to socalled Topos theory. This representation is homogeneous, variablefree, and uses only one inherent operation (concatenation of arrows). The semisymbolic level can be used to train a feedforward neural network that is not only learning the logical input theory, but rather the logical closure of this theory, i.e. a model of the underlying theory.  
Comparing algebraic frameworks used in AI  
Rulebased and procedural approaches are dominating the field of reasoning in artificial intelligence. Examples are methods used for inductive, casebased, or qualitative reasoning. Alternatively, algebraic approaches can be used. Such approaches focus on structural representations and provide a powerful and formally sound foundation for inference algorithms. Examples are the usage of antiunification in analogy and induction or relational algebras for qualitative reasoning.  
Assessment of solutions for programming assignments in PROLOG and LISP  
Assessing assignments and exams produces a big workload for lecturers and tutors.  
Flexible Knowledge Basis for Heterogeneous Data  
With the Problem of handling big heterogeneous (Web based) data new approaches of storing, retrieving, 