Invited talks
 
 

Modelling and Querying Interaction Networks in the Biochemical Abstract Machine BIOCHAM
François Fages (INRIA Rocquencourt)
In recent years, molecular biology has engaged in a large-scale effort to elucidate high-level cellular processes in terms of their biochemical basis at the molecular level. The mass production of post genomic data, such as ARN expression, protein production and protein-protein interaction, raises the need of a strong parallel effort on the formal representation of biological processes. We shall present the Biochemical Abstract Machine BIOCHAM and advocate its use as a formal modeling environment for networks biology. Biocham provides a precise semantics to biomolecular interaction maps. Based on this formal semantics, the Biocham system offers automated reasoning tools for querying the temporal properties of the system under all its possible behaviors. We shall review the main features of Biocham and report on our modeling experience with this language. In particular we shall report on a model of the mammalian cell cycle's control developped after Kohn's map.
 
Dr. Hab. François Fages leads the Contraintes group at INRIA Rocquencourt. His research is focused on Constraint Programming languages, from their fine relationship with mathematical logic (including linear logic), to new proof methods, type systems and new execution models (combining constraint propagation with state change), and on two application domains: combinatorial optimization problems and Systems Biology. Before his recruitment at INRIA in 1999, Francois Fages was a permanent researcher at CNRS Ecole Normale Supérieure in Paris since 1983, the date of his Doctorate Thesis. His main research interests have been unification theory, automated deduction, rule-based reactive programming and non-monotonic reasoning, constraint logic programming, concurrent constraint programming and systems biology.


Impact and Perspective of Planning Techniques in Robotics
Malik Ghallab (LAAS-CNRS)
Planning techniques can contribute to robotics by making it easier to program a robot and by augmenting its autonomy, usefulness and robustness. Planning is certainly needed if a robot has to face a wide diversity of tasks and/or a variety of environments. When planning is integrated within a robot, it usually takes several forms and is implemented throughout different systems, often relying on domain-specific representations and techniques. Among these various forms of robot planning, there is in particular path and motion planning, perception, planning navigation planning, manipulation planning, and domain independent task and mission planning. This talk illustrates some of these techniques and discusses their impact and perspective for the development of Robotics.
 
Prof. Ghallab is director of the LAAS-CNRS institute in Toulouse. His research interests in robotics and AI are in the integration of perception, action and reasoning capabilities within autonomous robots. His activity is mainly focused on mobile robotics, within experimental projects and research areas such as perception, control, planning and decision making. He contributed to topics such as object recognition, scene interpretation, heuristics search, pattern matching and unification algorithms, knowledge compiling, temporal planning and supervision systems.


Machine Learning for Autonomous Robots
Martin Riedmiller (University of Osnabrueck)
In recent years, many successful applications of machine learning methods have been developped, e.g. in classification, diagnosis or forecasting tasks. However, for a broad acceptance of learning methods as a standard software tool, still many theoretical and practical problems have to be solved. This is especially true for Reinforcement Learning scenarios, where the only training signal is given in terms of success or failure. Although this paradigm is in principle very powerful due to the minimal requirements on training information, today real world applications often fail due to the large amount on training experiences, until a task is successfully learned. Our research effort lies in narrowing this gap by developing methods for data-efficient and robust machine learning. Autonomous robots - some of them with the ability to play soccer - are one of our favorite testbeds. Examples of efficient machine learning methods and their integration into large software systems are shown on several (real world) tasks.
Prof. Dr. Martin Riedmiller is chair of the neuroinformatics group at the University of Osnabrueck, Germany. His research interests are machine learning and robotics. An important goal of his research activities is to make machine learning algorithms successfully work in practical applications. Several industrial projects resulted from this, e.g. forecasting systems for financial markets and newspapers sales rates. One of his main current research focuses is the investigation of reinforcement learning algorithms for complex real world applications, especially for autonomously learning robots. His robotic soccer team 'Brainstormers', a hybrid approach of machine learning, AI and conventional program code, was always among the best three teams during last years international competitions.


Computer supported Mathematics
Jörg Siekmann (DFKI, University of Saarbrücken)
The year 2004 marks the fiftieth birthday of the first mathematical theorem to be proved by a computer: “the sum of two even numbers is again an even number” (with Martin Davis’ implementation of Presburger Arithmetic) Classical theorem proving procedures of today are based on ingenious search techniques to find a proof for a given theorem in very large search spaces – often in the range of several billion clauses. The shift from search based methods to more abstract planning techniques opened up a new paradigm for mathematical reasoning on a computer and several systems of the new kind employ a mix of classical as well as proof planning techniques. In my talk I shall trace some key ideas of the past and then concentrate on current systems based on proof planning, using ΩMEGA, a mathematical assistant system and ActiveMath, a mathematical learning environment as case studies for demonstrating current strengths (and weaknesses) of the field.
Jörg Siekmann is a scientific director of the German Research Centre for Artificial Intelligence (DFKI) at Saarbrücken and one of the founders of A.I. in Germany. His research interest in automated reasoning is mainly focused on proof presentation, graph based theorem proving, knowledge representation for mathematics and proof planning as well as unification theory. As a scientific director of the DFKI and a professor of computer science at the Universität des Saarlandes he is the head of four research labs: multi agent systems, safety and security verification, automated reasoning and e-learning, each encompassing about a dozen researchers enjoying the interdisciplinary nature and cross fertilization of research on the borderline between these areas.


SmartWeb
Wolfgang Wahlster (DFKI, University of Saarbrücken)
Fortschritte auf dem Gebiet der mobilen Breitbandkommunikation und des semantischen Web ermöglichen innovative Internetdienste, die sich durch Personalisierung und Lokalisierung auszeichnen. Die ontologische Beschreibung von Webdiensten sowie die Informationsextraktion mithilfe der Sprachtechnologie ermöglicht den Übergang vom Suchen zum gezielten Finden von Information und Diensten im Web. Der Vortrag illustriert die neuartigen Informatik-Methoden anhand praktischer Beispiele aus dem Bereich UMTS-Mehrwehrtdienste, Ambient Intelligence und Infotainment im Automobil.
Prof. Wahlster ist der Leiter des Deutschen Forschungszentrums für Künstliche Intelligenz und Professor für Informatik in Saarbrücken. 2001 wurde er mit dem Zukunftspreis des Bundespräsidenten ausgezeichnet. 2003 wurde er in die Nobelpreisakademie in Stockholm aufgenommen. Er leitet die ISTAG-Arbeitsgruppe zur Definition der Förderthemen für das siebte EU-Rahmenprogramm.