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TU Wien Fakultät für Informatik KBS Knowledge-Based Systems Group
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Research Areas

Our research focuses on foundations and formal aspects of knowledge-based systems and Artificial Intelligence, with emphasis on (but not restricted to):


Knowledge Representation and Reasoning

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 and Complexity

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.

Declarative Problem Solving

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.

Intelligent Agents

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

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.

Knowledge-Based Systems in Engineering

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.

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