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CoIL - Computational Intelligence and Learning

Cluster of four European Networks of Excellence Network objectives and results

The goal of the CLUSTER Computational Intelligence and Learning is to achieve scientific, technical and "social" integration of four communities that perform research, development and application:

• ERUDIT - Fuzzy logic
• EvoNet - Evolutionary computing
• MLNet - Machine learning
• NEuroNet - Neural networks

Networks of excellence for these areas exist and have existed for some time. Actions of the CLUSTER Computational Intelligence and Learning will be aimed at educating networks about the concepts and techniques of other networks, comparing techniques and theory, searching for possibilities to improve access by industry to a wider range of methods and tools than is provided in the context of a single network and to integrate techniques from different fields into new new techniques. In particular an inventory will be made of the needs for new technology in industry to match this with available technology at the scientific nodes.

This will be achieved by:

Organising meetings on the use of adaptive computing systems in particular sectors in industry. This should broaden the scope of methods and systems that are applied in practice and lead to a better understanding of the strengths and weaknesses of various approaches. Structuring this by sector is more effective than structuring this by scientific approach or technique.

Improving (electronic) communications between networks

Providing tutoring materials for students, practitioners and scientists from other areas. This should make it possible to become acquainted with an area and provide a basis for access to the scientific literature and for the use of tools.
Outlining research and development directions aimed at integrating techniques from the participating networks. Theoretical work is necessary for a more basic understanding of the relations between different types of techniques and the way to select one or to combine techniques.
Scientific benefits will be theoretical, technical and "cultural" integration of the fields and networks participating in the CLUSTER Computational Intelligence and Learning. Industry will benefit by gaining access to a wider range of techniques and better understanding of the applicability of tools.

Strategy planning

The mission of the CLUSTER Computational Intelligence and Learning action is to achieve closer cooperation between the areas of machine learning, case-based reasoning, knowledge acquisition (in MLNET), fuzzy logic (ERUDIT), evolutionary computing (EvoNet) and neural network computing (NEuroNet).

There is an overlap in potential applications for these methods and theoretically there are relations between different paradigms that can be explored. The terminology and methodology of these areas is rather different and this makes collaboration and integration difficult.
The strategy to achieve this is to explain the main concepts and methods underlying tools and techniques in the various fields to the other participants and to exploit the elements that have been created by the participating networks (teaching materials, contacts with industry and last but not least: electronic information services). This should lead to greater awareness of other techniques, to more collaborative research and to integration of tools and techniques.

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