Research Advisor (Directeur de Recherche ONERA: THE FRENCH AEROSPACE LAB), Chatillon, France
Belief Functions in Information Fusion Processes
The increased complexity of environments and operational needs requires information systems to process more and more disparate complementary sources, in order to provide a variety of information of higher level to different interactive components, whatever the organization they serve. Therefore problems arise mainly because of unsuitable, unreliable and heterogeneous data, conflicts in their interpretation, disparities in the frames of reference to consider, ambiguous associations, logic of combining, and decision principles to follow.
A federative view of coherent operators that allow facing globally such a situation is provided in the framework of Belief Functions. The approach is based on a generic operator named “extension” that propagates knowledge from one frame of discernment to another, thanks to uncertain or imprecise relations that can be expressed between their elements. Beyond the benefit of its direct implementation, this operator provides a general formulation of different operators that constitute a complete, coherent and adaptive processing of multiple uncertain observations, from their modelling up to the required decision making.
The particular conditions that lead to the traditional operators can be specified, and a few examples illustrate a suitable management of uncertainty processing thanks to the available tools.
Assistant professor at the University of Silesia, Institute of Mathematics
Habilitation in Computer Science, Systems Research Institute, Polish Academy of Sciences in Warsaw
Fuzzy Implications Functions: Recent Advances
Fuzzy implication functions generalize the classical implication and play a significant role in the development of fuzzy systems. The study of this class of operations has been extensively developed in the last 30 years from both theoretical and applicational points of view. Due to the importance and a constant growth of interest, it becomes a natural need to present a coherent knowledge about this class of functions.
In our talk we will firstly discuss some historical aspects of fuzzy implications. Next, we will describe main classes of implications functions, showing different methods of generating them from other connectives used in fuzzy logic or from unary functions defined on the unit interval. In this part we will present main characterizations and representations results and we will discuss also the problem of intersections of different classes of fuzzy implications. Finally, we will concentrate on recent topics connected with fuzzy implications. In particular we will show how knowledge of the solutions of certain functional equations can help in designing methods that increase the computing performance of different inference schemes used in approximate reasoning.
Principal Researcher at the European Centre for Soft Computing
Associate professor at the University of Granada, Dept. of Computer Science and Artificial Intelligence
An automatic method for forensic identification based on soft computing techniques
This talk will be focused on the development of an automatic method to assist forensic anthropologists in the identification of deceased people through craniofacial superimposition. This skeleton-based forensic identification technique is based on overlaying a photograph of the missing person and a graphical model of the skull found to determine if they correspond to the same person. The introduced intelligent system considers the use of fuzzy logic and evolutionary algorithms for the automatic reconstruction of tridimensional models of human skulls and for the automatic 3D skull model – 2D face photograph overlay. The resulting soft computing-based system is the outcome of a coordinated project between the European Centre for Soft Computing and the University of Granada’s Physical Anthropology Lab. It is protected by an international patent and will be commercialized in 2012. The results obtained in several real-world cases solved by the latter Lab in cooperation with the Spanish Scientific Police will be reported.
Auke Jan IJSPEERT
Associate professor at the EPFL (Ecole Polytechnique Fédérale de Lausanne)
Head of the Biorobotics Laboratory (http://biorob.epfl.ch)
Adjunct faculty at the Department of Computer Science at the University of Southern California
Control of locomotion: from biology to robotics
The ability to efficiently move in complex environments is a fundamental property both for animals and for robots, and the problem of locomotion control is an area in which neuroscience and robotics can fruitfully interact. Animal locomotion control is in a large part based on central pattern generators (CPGs), which are neural networks capable of producing complex rhythmic patterns while being activated and modulated by relatively simple control signals. These networks are located in the spinal cord for vertebrate animals. In this talk, I will present how we model CPGs of lower vertebrates (lamprey and salamander) using systems of coupled oscillators, and how we test the CPG models on board of amphibious robots, in particular a salamander-like robot capable of swimming and walking. The models and robots were instrumental in testing some novel hypotheses concerning the mechanisms of gait transition in vertebrate animals. I will also present control architectures based on coupled dynamical systems that we use to control the locomotion of various robots (quadruped, humanoid and reconfigurable modular robots) as well as exoskeletons for patients with locomotor deficits.
Emeritus Professor at Tokyo Institute of Technology
Emeritus Researcher at European Centre for Soft Computing
What Kinds of Uncertainty Are We Aware of? − Categories and Modalities of Uncertainty −
As a conventional concept of uncertainty, we are familiar with the ‘probability’ of a phenomenon. Also we often discuss the ‘uncertainty’ of knowledge. Recently, Fuzzy Theory has brought a hidden uncertainty, ‘fuzziness’, to light. Reflections on these ideas lead to a fundamental question: What kinds of uncertainty are we aware of? Motivated by this question, this study aims to explore categories and modalities of uncertainty. For instance, we have found that (i) ‘form’ is a category of uncertainty; (ii) ‘inconsistency’ is a modality of uncertainty; (iii) the inconsistency of form is one of the major uncertainties. We elucidate seven uncertainties (or nine if subcategories are counted) and identify three essential ones among them, such as the fuzziness of words. We also discuss some linguistic and logico-philosophical issues concerning uncertainty. Finally, the structure of uncertainty will be shown.