Institut für Kognitionswissenschaft

Institute of Cognitive Science


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Some topics for BA and MA theses

 

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:

  • A narrative theory based measure of the quality of the plot called tellability needs to be computed based on a graph representation of the produced plots, and its validity needs to be empirically evaluated.
  • The employed character architecture includes a cognitively inspired model of personality traits and affects, which can be used to explore a space of possible plots. Yet it remains to empirically study whether different personality parameters result in a reader-impression of different personalities in characters, and whether readers can correctly derive character's moods and emotions from their actions in the plot.
  • 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 as a MSc thesis.
Contact: Leonid Berov

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.

Design and implementation of the Structure Mapping Theory

 

The structure mapping theory by D. Gentner is a well-known psychological theory for finding analogies and making analogical inferences. Although it is today's most prominent approach for analogies, the existing implementation of the structure mapping theory from the 1980s uses rather old technology.
This thesis shall examine the current state of the structure mapping theory and develop an algorithm to implement the analogical mapping process. The practical work -modelling and implementing the software- will play a significant part in the thesis.

A classification of analogies - types, fields of applications

 

Humans use analogies in different situations and for different purposes. In science, new problems are understood and explained via analogies by well-known phenomena. Analogies are also used in education: e.g. mathematical operations on polynomials can be explained via analogies to operations on natural numbers. Analogies and metaphors are very important stylistic devices in linguistics, e.g. in poems, tales and fairy stories. We use analogies in our everyday life to solve new problems.
This thesis shall review literature to give an overview of analogy usage and analyze the different types of analogies. The student shall propose criteria for a classification 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.

Intensions as Algorithms

   

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.