Our research focuses on foundations and formal
aspects of knowledge-based systems and Artificial Intelligence, with
emphasis on (but not restricted to):
Representation of knowledge in a suitable form and methods for
reasoning from a given knowledge base are at the core of any
knowledge-based system. Our research deals with a variety of issues
in this context, among them nonmonotonic and preferential reasoning,
reasoning about actions and causality, modeling and query answering, handling incomplete and
inconsistent information, abductive and diagnostic reasoning, and
updating knowledge bases.
Computational logic may be understood as the usage of logic in
Computer Science, which has a great tradition and paved the way to
fields such as relational database systems, programming language
semantics, and functional programming, to mention a few. Our research
in this area deals with several issues including logic programming, description logics,
ontology based data access, calculi for classical and non-classical logics, second-order logic,
knowledge base optimization and simplification, proof-theory for
nonmonotonic logics, and computational complexity analysis.
Many efforts have been spent to develop problem solving methods in
which solutions are described in terms of desired properties (i.e.,
how a solution looks like) rather than computing them by means of an
explicit algorithm. Our work on this currently centers around Answer Set
Programming, which is a recent problem solving paradigm, but also addresses
declarative planning and applying SAT and QSAT solvers for solving advanced
reasoning tasks more quickly. Furthermore, we are interested in
language extensions which allow for a more user-friendly and compact
problem formalization.
The rise of the software agent paradigm has renewed the interest in
autonomous agents with high problem solving, social, and communication
skills. Intelligent software agents in particular should have strong
reasoning capabilities which allow them to draw conclusions about the
world from an internal representation, to deal with assumptions and
beliefs, and also to plan activities, to mention a few. Our research
interests in this area includes declarative action policies and programming languages
for agents, reasoning modules for knowledge-based agents, and
game-theoretic methods for dealing with environments with uncertain and partial observations.
Mobile robots have gained an increasing attention over the last years,
notably because of soccer events like RoboCup and
FIRA, or the Mars mission. Besides
wheel-based robots, animal-like and biologically-inspired walking
robots are in the focus of attention. We research methods to build
small and light-weight legged robots like
NANO and to improve their ability to act
autonomously.
Intelligent measurement systems is a research direction in engineering
which is gaining increasing attention. Human operator intervention in
traditional measurement systems should be reduced for various reasons
while high quality measurement processing is maintained. In
interdisciplinary projects with colleagues from TU Vienna's Engineering Geodesy
Group, we are researching models and knowledge-based methods for
building intelligent measurements systems in the video measurement
domain.
