1. University of Alabama, Huntsville

May 3 and 4, 2007

For details go to
UAH Workshop

Four lectures discuss key elements of a new technique for constructing simple explanations from numerical data. Applications arise in many areas such as bioinformatics, economics, engineering, finance, and medicine.

Particular focus is on the very difficult situation where comparatively few data records with numerous data elements per record are given.

The workshop makes use of the following material: The book "Design of Logic-based Intelligent Systems" by K. Truemper (Wiley, 2004), and the technical paper "Obtaining Explanations from Numerical Data" by K. Riehl and K. Truemper.

The technique has been implemented in the explanation modules of the Leibniz System software . The workshop covers the use of that software.

1. Institute IASI-CNR, Rome, Italy

Week June 7-11, 2004

For details contact
rinaldi@iasi.rm.cnr.it

2. University of Cologne, Cologne, Germany

Week September 6 - 10, 2004

For details contact
mjuenger@informatik.uni-koeln.de

3. Institute ZIB, Berlin, Germany

Week September 27 - October 1, 2004

For details contact
groetschel@zib.de.
Here is the
Workshop
Schedule

4. Technical University, Munich, Germany

September 19, 2005

For details contact
gritzman@mathematik.tu-muenchen.de

September 27, 2005

For details contact mjuenger@informatik.uni-koeln.de 6. Technical University, Munich, Germany

March 8, 2006

For details contact gritzman@mathematik.tu-muenchen.de

An intelligent system is a system that accomplishes tasks that, when carried out by people, require significant intelligence. Example tasks are medical diagnosis, processing of natural language, and supervision of complex processes.

The lectures cover the design and implementation of intelligent systems that are based on an extension of propositional logic. The lectures do not assume any prior knowledge of logic.

Topics of lectures:

- Levels of thinking such as thinking about problems (1st level)
and thinking about thinking (2nd level)

- Expert systems and intelligent agents as special cases
of intelligent systems

- Logic techniques such production rules, Prolog, propositional
logic, first-order logic, fuzzy logic, Bayesian networks

- Propositional logic with extensions

- Problems SAT and MINSAT

- Theorem proving

- Uncertainty of statements and conclusions

- Learning logic from data, including transformation of
rational data and set data to logic data

- Logic problems at the second level of
the polynomial hierarchy

- Question-and-answer processes

- Systems that construct systems, which are examples of
thinking about thinking