Developing an interactive tool for course-of-study visualization

The SIDDATA study assistant software utilizes state-of-the-art AI paradigms to assist students in finding their individual study goals and in realizing these goals by recommending educational resources, single events or on-campus resources such as the student helpdesk. One core functionality of the study assistant is to aggregate data of the individual student and make this data available for the student in a comprehensive manner, while adhering to strict data protection guidelines and ethical considerations concerning individual autonomy. In order to make the assistant software more transparent for the user, various methods have been studied. One such method is the visualization of study data. By consolidating literature on human computer interaction, a visualization framework for study data is to be developed and implemented in prototype form into the study assistant software. Applicants must have decent skills in programming, preferably python, and must have a high interest in working in an interdisciplinary research environment. Rudimentary skills in Django and vue.js are not necessary but recommended.

Contact: Johannes Schrumpf

Computational Storytelling using Multi-Agent Simulation

To model computational creativity in the domain of stories, one first needs to develop a computational representation for these multi-faceted and dynamic works of ingenuity. We approach this by representing a narrative's story-world and characters as a multi-agent 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.
Our system can be extended in several ways, which are all potential thesis projects:

Stories are commonly taken to consist of two layers: the content-plane (what is told, plot) and the description-plane (how it is told, discourse). Our storytelling system focuses on the generation of plot, which is represented as a directed, acyclical graph containing events: actions, happenings and important changes in characters' affect. Translating this graph into storytext would be the main objective of a thesis that focuses on the discourse layer. This can be extended in several interesting ways: Generating aesthetic text, selecting and reordering which events are presented in the discourse, focalizing the discourse on the perspective of a specific character and its perceptions and extending the institute's Pepper robot with actual storytelling capabilities are just a selection. Depending on scope and approach this is suitable for both BSc, and MSc projects. Fictional characters are social (paper) beings, they constantly try to outwit, deceive or simply understand each other. An important property of fictional minds should thus be a mind reading ability, a "Theory of Mind". Implementing a domain-independent ToM on the architectural level of the system is thus important to generate most interesting tales. Achieving this through a connection of planning and recursive simulations could be explored by a challenging MSc thesis. Many stories serve the purpose of representing a characters mental and social maturation process. This means that (some) characters need to be able to change through the course of a plot. Learning and character development are two phenomena that could be integrated into the storytelling system, to allow it to model a broader range of narratives. Such a thesis would need to perform narratological, theoretical research as well as practical implementation work and is better suited for a MSc thesis. Characters act based on the beliefs they possses about the story world. One plotting strategy that can be emplyoed to affect how autonomous characters act is to control the destribution and flow of knowledge in the narrative system. This includes meta-reasoning processes that decide which character should posses which (incorrect?) beliefs, as well as a analysis and implementation of general knowledge accquisition stratagies that characters follow in the face of uncertainty. This thesis topic woult thus include the review of literature in narratology and psychology, as well as as well as practical implementation work. It is best suitable for self-directed individuals who can tolerate uncertainty and open-ended problems. It could be addressed by MSc or strong BSc candidates.Framing is a task in the computational creativity framework that is used to represent the output of a creatve process in a new light. The weak framing hypothesis says that a generated artefact is perceived as more creative when it is presented with a framing. This hypothesis has not been tested empiricially. In prior work we demonstrated that stories can be framed by functional summaries, and implemented an algorithm for the summarization of stories. The task of this thesis would be to empirically test the weak framing hypothesis. This would involve minor programming tasks, but mostly the design, implementation and evaluation of the study. It is thus located on the boundary of AI and experimental psychology. Best suited for BSc. candidates, Currently in progress: The plot of a story consists not only of characters actions, but also of happenings: events that have no agent but a patient, like coincidences or natural disasters. These are relevant, as they can resolve narrative equilibria and drive characters actions. Exploring the usability of Reinforcement Learning to decide which happening should happen when, and to who would be an interesting open-ended thesis on the interface between symbolic AI and subsymbolic ML. Its suitable for a technically savvy Bachelor's student, or Master's student capable of autonomously expanding their goals and work.

Contact: open

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:

One can connect abstract symbols used in classic AI systems (represented as concepts) to lower-level semantic features (represented as dimensions of the space). Machine learning algorithms can be readily applied, as they typically also make use of a feature space. As the individual dimensions of the conceptual space are cognitively meaningful, the resulting regions can be interpreted. The whole approach is grounded in psychological findings, making it cognitively plausible.

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:

There are different types of reasoning in conceptual spaces, all of them based on a geometric approach. The goal of a thesis in this area is to algorithmically define, implement, and evaluate these reasoning techniques in the context of our formalization. Potential subtopics include:

Concept combination: Combining existing conceptual regions in order to derive the meaning of compound terms like “small green apple”. Similarity-based reasoning: Using conceptual similarity to implement common sense reasoning strategies – if you like oranges, then you might also like mandarins. Interpolative reasoning: Using conceptual betweenness to implement common sense reasoning strategies – if both bachelor and PhD students have to pay an enrollment fee, this probably also applies to master students.

Potentially, existing concepts in a given conceptual space can scaffold learning from very limited data. In one-shot learning, a new conceptual region is learned based on a single example. This can potentially be achieved by generalizing from existing conceptual regions that partially fit the given example. In zero-shot learning, a new conceptual region is learned without any examples, but based on an abstract description of the concept (e.g., “oranges are sweet, round, orange and weigh about 130 g”). A thesis in this area defines, implements, and evaluates an algorithm for solving these learning problems in the context of our formalization. A huge fly is a lot smaller than a tiny house. The meaning of relative properties like “huge” and “tiny” depends on the contrast class it is being applied to (i.e., “fly” or “house”). The goal of this thesis project is to extend our formalization such that it can also represent such relative properties. Conceptual knowledge always includes information about part-whole relations (e.g., a car has four wheels). There has already been a proposal about how to represent such relations in conceptual spaces, however this proposal has never been implemented. The goal of this thesis project is to incorporate this proposal into our formalization, to implement it, and to investigate whether it is feasible and useful from a practical point of view. One can also investigate whether this can generalize to more kinds of relations.

[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 E-Learning Platforms

Heuristics for theory projection in analogical reasoning

Heuristic-Driven 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.
Several steps in this process are based on heuristic knowledge. The thesis shall propose and implement different heuristics and examine their value in process of analogy making.

The semantics of theory projection

Heuristic-Driven Theory Projection (HDTP) is based on an extended form of anti-unification of logical theories. It introduces certain second-order elements which seem to be well-founded 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

Heuristic-Driven Theory Projection (HDTP) is a framework for analogical reasoning currently under development at the IKW. The representation is essentially logic-based. 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.
Besides HDTP exist many other approaches to analogical mapping which use different representational formalisms and have different underlying assumptions how analogies occur. E.g., according to the Structure Mapping Theory (SMT) by Gentner, a domain is represented as a structure (a graph) with objects, attributes and relations. Analogies are identified via structural commonalities. The Analogical Constraint Mapping Engine (ACME) by Holyoack and Thagard uses neuronal networks to identify mappings. In turn, Indurkhya and Dastani use an algebraic framework for representation of domains.
The thesis shall select a number of approaches and compare them against HDTP with respect to their analogical mapping and inference mechanisms.

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.
The findings can be compared to other learning strategies (e.g. inductive, abductive and deductive reasoning) or they can be tested against the analogical inference mechanisms in HDTP.

Learning first-order logical theories with neural networks in reasoning processes

The well-known 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 so-called Topos theory. This representation is homogeneous, variable-free, 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.
The thesis shall develop this approach further by applying it to complex logical input theories in reasoning and planning domains.

Comparing algebraic frameworks used in AI

Rule-based and procedural approaches are dominating the field of reasoning in artificial intelligence. Examples are methods used for inductive, case-based, 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 anti-unification in analogy and induction or relational algebras for qualitative reasoning.
The thesis shall compare different algebraic methods with respect to their expressive strength and other theoretic features.

Assessment of solutions for programming assignments in PROLOG and LISP

Assessing assignments and exams produces a big workload for lecturers and tutors.
Certain types of assignments like multiple choice exercises, can be easily corrected automatically.
Other types of exercises, e.g. programming exercises, can hardly be assessed completely automatically.
Nevertheless, it is possible to support the assessment of such exercises by methods comparing
the handed-in solutions with exemplary solutions on different levels: structurally and functionally.

Flexible Knowledge Basis for Heterogeneous Data

With the Problem of handling big heterogeneous (Web based) data new approaches of storing, retrieving,
and reasoning were developed. These methods differ from the classical relational data model.
Methods from the semantic web field allow data bases which directly use xml structures for data representation or to use very flexible triple storages.