MLNET-II

 

Programme of work for a Network of Excellence in

Machine Learning and Related Fields

Part 2

 

 

1998

 

 

 

 

 

PART 2: DESCRIPTION OF THE NETWORK OF EXCELLENCE

 

 

 

1. Preface

The description of the network consists of two parts, a part on MLNET and a part on the CLUSTER "Computational Intelligence and Learning". MLNET is effectively a continuation of the network of excellence in Machine Learning MLnet. MLnet was active in the period 1993-1997. The CLUSTER part is a part of the work programme that concerns the cooperation between four related areas, each represented by a network of excellence: Machine Learning, represented by the actual MLNET, evolutionary computing, represented by EvoNet, neural network computing, represented by NEuroNet and fuzzy logic represented by ERUDIT. Each chapter contains a section on MLNET and a section on the CLUSTER "Computational Intelligence and Learning".

 

 

2. Network objectives and results

2.1. MLNET

 

The general aim of MLNET is to support the invention, analysis, technological development and application of flexible, adaptive and knowledge intensive information technologies. These technologies encompass techniques and methods from the fields of Machine Learning, Knowledge Acquisition and Knowledge Modelling, Knowledge Discovery and Data Mining in Databases, Case-Based Reasoning and Learning, Learning Robots and Statistical Techniques for analysis of large data sets.

The main long term goals of MLNET are :

  1. to stimulate and support the transfer of Machine Learning and related technologies from academia to industry,
  2. to stimulate the translation from industrial needs to technical and scientific problems,
  3. to represent and coordinate the wide variety of ML related research areas, which include: data mining and knowledge discovery in databases, knowledge acquisition, case based reasoning, learning robots statistical approaches to learning,
  4. to disseminate information, research and application results about ML and related fields,
  5. to represent the European ML community world wide,
  6. to enhance the training in ML and related technologies at the European level,
  7. to improve cooperation with related fields, in particular Inductive Logic Programming, Data bases, Neural networks, Evolutionary Computing, Natural Language Processing and Fuzzy Logic.

 

Specific goals of MLNET are:

To achieve these goals, specific tasks will be carried out by committees. The nature and scope of these activities will be defined by a steering committee that will also monitor progress. The network will be open to institutes and companies that are active in the area of the network and that are prepared to take part in network activities.

The scientific community will benefit by a better overview of ongoing research and better access to resources such as documents, software, datasets, teaching materials. Industry will benefit by better access to available technology and increased awareness of the use of computational learning techniques.

 

2.2 The CLUSTER Computational Intelligence and Learning

 

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 in four related areas: machine learning, fuzzy logic, evolutionary computing and neural net computing. 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:

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.

3. Strategy plan

 

 

3.1. MLNET

 

 

The current historical moment is a delicate one for Machine Learning. In fact, there are two opposite emergent trends: on the one hand, there is a need, and a consequent request, to move ML methodologies from the academic environment to large scale applications. To satisfy this request, joint efforts to build up effective and flexible systems, by integrating methodological contributions both from inside and from outside the field, are likely to be necessary.

However, this requirement of unification is contrasted by the opposite tendency, which we are experiencing, of a fragmentation of the field into a multiplicity of rather isolated communities, each privileging a specific type of approach. This tendency has the further negative effect of favouring duplicated exploration of the same paths, with a consequent waste of useful energies.

Another relevant aspect, recently becoming apparent, is the sudden broadening of the potential applications domains for ML techniques: KDD (Knowledge Discovery in Databases), Data Mining in a number of industrial, commercial and legal domains, molecular biology, natural languages, learning from texts, information retrieval, just to mention some. The complexity of the problems requires the joint work from several disciplines, including experts from the application domain. The task of MLNET is to demonstrate the potential of its technologies for such applications and to embed them in practical methodologies and tools.

We may also notice a more concrete interest in studies on learning in Cognitive Sciences, with the possibility of joint research projects. In this variegate landscape, an institution such as a Network of Excellence will play a fundamental role in keeping connections, promoting cooperation and broadcasting information both among the emergent research themes, and between the developers and potential users of ML programs.

 

 

 

3.2. CLUSTER Computational Intelligence and Learning

 

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.

4. Action planning

 

 

4.1. MLNET

 

 

The activities of MLNET will consist of a set of actions defined below. Some actions have an open character to leave room for initiatives that are proposed by members. Proposals for new actions must fit the general conditions and the constraints of the budget and must be approved by the Management Board.

 

A1: Annual report:

 

An annual report will be produced that is readable and accessible for a wide audience. This will replace the quarterly newsletter of MLNET-I. It is intended to complement the more fluent information that will be available at the online information service. The annual report is therefore not directed at members of the network but at those who are interested in its activities such as potential new members, researchers and industries in adjacent fields, administrators of universities and science research councils, branch organisations industry. The annual report will be produced by the coordinating node. It will include an overview of network activities, a short financial report and an overview of the main developments in the fields covered by the network. The network will compile a distribution list in collaboration with other networks and members. The first annual report will be completed after 13 months (1 month after the first year). The second annual report will be completed after 25 months (1 month after the end of the second year).

 

A2: Online Information Services

MLnet-1 has developed a very successful information system that contains a variety of information including data sets, software, descriptions of the nodes in the network and their specialisations. The purpose of this task is to maintain this WWW infrastructure, to extend it to include new information and to improve it with the latest technology. Specific activities in this task are:

 

 

GMD will continue to provide its WWW server fee of charge. To maintain and extend the information service requires 0.5 person/year.

 

A3: Technological Roadmap

A document will be produced outlining the current State of the Art of the field in Europe, outlining the most promising lines of research, development and application. This can then be used as a basis for coordinating research. The Technology Roadmap will be a major deliverable of the Network. A preliminary version of a Roadmap has been produced during the preparation of MLNET-II and this can act as a starting point. After about four months a meeting will be organised (the "MLNET summit") with senior researchers in the field, who will present their views of the field. These presentations will be used by the coordinator of this action to produce a written Roadmap document. The Roadmap will include an overview of the state of the art of scientific work, an overview of applications, main research directions and main technical "needs" from an industrial viewpoint. The Roadmap will be completed before the end of the first year. After about 18 months another meeting will be held to discuss and revise the Roadmap and on the basis of this an updated version will be written. This action will be carried out apart from the corresponding action in the CLUSTER Computational Intelligence and Learning but obviously, the first version of the Technological Roadmap that is constructed there, will be used in preparing the second MLNET Roadmap. It is a significant job to do this for Machine Learning and it is even more complicated to incorporate Case-Based Reasoning and Knowledge Acquisition. Producing and integrating all these roadmaps in one pass seems a job to be tried after this network has ended and the two roadmaps are stabilised. The budget for this activity will be spent on travel to the MLNET summits and on writing the document. The budget is based on the idea that the MLNET summits can be combined with meetings of the Management Board, so that only an additional travel budget is needed.

Schedule:

Month 4: MLNET summit 1

Month 9: Roadmap 1

Month 18: MLNET summit 2

Month 21: Roadmap 2

 

A4: Organisation of workshops and meetings

On an ad hoc basis the network will support or elicit proposals to organise workshops that help to achieve the goals of the network. As much as possible these meetings will be held in the context of other events such as conferences to enable representation of MLNET at these events. Written proposals for such meetings can be submitted to the management board that can allocate funds for organisation or travel. A proposal must include a motivation, a detailed plan and a budget. Proposals will be evaluated by the extent to which they contribute to the goals of the network.

 

A5: Guide to Industrial and Commercial Applications of Machine Learning

In cooperation with other fields that provide inductive techniques for adaptive systems (see CLUSTER part), MLNET will produce a document with guidelines for the application of Machine Learning and related technologies to practical problems. The purpose of this is to assist people from industry in evaluating if inductive techniques are suitable, and that provides methodological support for collecting and preparing data, selecting techniques and tools, applying these and interpreting the results. The document will include examples of successful projects. The budget for this action is to be used for meetings and for personnel to prepare a document (in hardcopy or electronically). The budget for this task is for organising meetings and personnel costs for writing the guide.

Month 9: Draft 1

Month 15: Final version

 

A6: Ambassador fund

One of the goals is to explain and demonstrate the potential of machine learning to industry, to (events of) other sciences and to meetings on the coordination of research. This is primarily directed at audiences who are not familiar with these techniques. The Ambassador fund is to support travel with this purpose. A member of an MLNET node can apply for travel funding.

 

 

A7: Education support

Organising education is not the main task of MLNET but coordinating and facilitating courses at a European level is part of its coordinating task. MLNET will support educational actions at a European level that complement existing education provided by universities and companies. Examples of such activities are summer schools and distance learning courses aimed at a wide European audience. A node can submit a proposal for support of such activities to the management board and this can decide to allocate financial support.

 

    1. CLUSTER Computational Intelligence and Learning

The actions to be undertaken by the CLUSTER are directed at comparing and integrating research, development and applications in the fields of the participating networks.

 

C1: Technological roadmap

 

 

The goal of this action is to produce an overview of recent technical and scientific results and lines of future development in the form of a "technological roadmap". This will outline the main commonalities and differences between the approaches, describe the state of the art of integration and selective application of different technologies and outline possibilities for synergy that are to be explored in scientific research and practical application. The emphasis will be on synergies that can be achieved by combining techniques.

 

To achieve this, a meeting will be organised in which experts from the participating fields present their views. On the basis of this a document will be prepared. A first document will be produced at the end of the first year and a revised and updated version by the end of the second year. During the second year a workshop will be held to discuss and revise the first version. ERUDIT will accept the editorial responsibility for this action. Contributions will have to be supplied by the other networks. Details of this will be arranged at the first management board meeting.

Each participating network will appoint 2 persons to take part in this action. The action will be coordinated by the coordinating node of ERUDIT. Results of this action will be two versions of a Technological Roadmap, one after the first year and one after the second year.

 

The budget for this action consists of 10 KECU per year for secretarial service and editing and 20 KECU per year for meetings and travel.

 

 

C2: Workshops and visits

 

The most effective way to integrate research and development between networks is to participate in each others activities and to organise common workshops. The CLUSTER Computational Intelligence and Learning will support this by organising a workshop on the integration of techniques for different types of adaptive systems and by supporting events that are organised by nodes from at least two but preferrably more different networks. Another form is the support of joint meetings of committees from different networks or the support of visits from members of one network to events of another network. Organisers of a workshop will produce a report. Depending on the occasion, the CLUSTER Computational Intelligence and Learning will fund travel or organisational costs of the workshop or visit.

 

 

C3: Benchmarking competitions

 

Many nodes are aware of different techniques within the scope of their own NoE to tackle a give problem. This action aims to increase the awareness of the techniques available from other NoEs through an annual competition. These competions will be organized on solving problems provided by industry, such as industrial nodes within CLUSTER networks. A specification of the problem and data will be made available to all nodes of the CLUSTER NoEs via the Web. Participants will be encouraged to use any applicable techniques (not just from the scope of the NoE to which they belong), or to combine techniques if appropriate.

Similar competitions have already been held successfully by ERUDIT and also by others. In this case, the goal is to use problems that are analoguous to real industrial problems and that are not all biased towards a particular approach. The main goal is to clarify the relations between different approaches, to stimulate the search for solutions that combine different methods and to continue to ensure an overall spirit of cooperation. Identifying real industrial problems that involve data that can be released to anyone will be difficult and part of the work in this action will have to be devoted to this.

Each competition will end with a workshop at which solutions will be presented, evaluated and discussed. A report will be produced on each workshop, with descriptions of the solutions and a discussion chapter. The publicity possible from such events can be used to show the value of considering approaches from outside a given node's NoE, and how these are often related.

This action will be coordinated by the coordinating node of NEuroNet.

 

C4: Accessible tutoring materials

 

The goal of this action is to provide tutorial materials on each of the participating fields for outsiders of the field. The terminology and methods used in the fields of the participating networks vary considerably and it is important to provide quick introductions to these fields that are accessible for researchers and developers that are familiar with the other fields. Each participating network will provide tutoring materials in the form of web-based "distance learning" courses and/or lecture notes with demonstrations. The first goal of this is to provide easy access for members of one network to the main concepts, theories and techniques of the other networks. First steps in this direction have been set by ERUDIT and EVONET. The teaching materials should include an overview of the main concepts and techniques, including also those that are not included in the tutorials.

Results: sets of tutoring materials that provide a basic introduction of the field and an overview of the main concepts for each of the particpating fields, aimed at scientists and practitioners of the other fields. This action will use materials that have been produced by participating networks or that are publicly available.

This task will be coordinated by Universiteit van Amsterdam or by the node from MLNET-II that coordinates MLNET action A7. The budget can be relatively small because some useful materials are already available.

 

 

C5: Communication

 

Existing electronic communication facilities of networks will be extended with exchange between networks. This involves including information on other networks in existing information systems and building new information sources to support actions of the CLUSTER Computational Intelligence and Learning.

 

 

C6: Coordination

 

This action involves financial administration, preparing and reporting steering committee meetings, reporting to CEU and general management.

 

 

 

 

5. Dissemination planning

 

Most actions described before have the character of dissemination of information and therefore no additional dissemination actions are needed.

 

 

6. Management and organisation

 

 

6.1. MLNET

 

MLNET makes a distinction "nodes" and "main nodes". "Main nodes" are nodes that have a sustained record of good research, development or applications and that contribute significantly to actions of the network. MLNET will start with the main nodes of MLnet - 1. The set of main nodes will be extended during the period in which MLNET is active. In particular it will include main groups in Case-Based Reasoning and Knowledge Acquisition.

The management structure of MLNET consists of (a) a management board with representatives of all main nodes and that will include representatives from each subcommunity, (b) a coordinator and (c) committees that undertake specific actions. MLNET will start with a list of initial recognised subcommunities: (Knowledge acquisition and Case Based Reasoning). These subcommunities identify with the goals of the network, are of significant scientific size and stability and have already elected a representative for the MLNET management board. Subcommunities entirely within Machine Learning will not normally be recognised as subcommunities of this type, but will be represented by the main nodes as in MLNET-I. The list of recognised subcommunities will be evaluated by the management board annually. New subcommunities can organise themselves and apply for the status of recognised subcommunity which will be granted by the management board provided there are both a significant level of technical activity within the community together with a significant amount of overlap with the goals of MLNET. In this fashion, MLNETís management structure will continue to evolve at the same pace as the scientific community evolves. By this means, smaller subcommunities will have access to the benefits of the network without having to propose their own networks, and the network will continue to include the best and most relevant efforts related to Machine Learning.

 

Initial main nodes: IIAI Barcelona, Daimler-Benz AG, GMD, Syllogic BV, University of Aberdeen, Universiteit van Amsterdam, Universitat Dortmund, University of Dublin, University of Ljubljana, Universite Paris-Sud and Universita di Torino.

 

Initial management board:

The management board will consist of representatives of all nodes and will include representatives of each subcommunity. The initial management board will consist of:

Prof. B.J. Wielinga (Universiteit van Amsterdam, Knowledge Acquisition)

Prof. L. Saitta (Universita di Torino, A3: Technological Roadmap)

Dr. S. Wrobel (GMD, A2: Online Information Service)

Prof. M. Keane (University of Dublin, Case-Based Reasoning)

Prof. Y. Kodratoff (Universite Paris-Sud)

Prof. K. Morik (Universitat Dortmund)

Dr. R. Nakhaeizadeh (Daimler-Benz AG)

Dr. P. Adriaans (Syllogic BV)

Prof. I. Bratko (University of Ljubljana)

Drs. M. van Someren (Universiteit van Amsterdam, coordinator)

The management board will meet at the beginning of the first year, at the end of the first year and at the end of the second year of the network. In the meantime communication will be by email.

 

Coordinator

The task of the coordinator is to handle the financial administration, to coordinate reporting to the CEU, to organise the work, meetings and email contacts of the management board.

 

Action committees

The actions of MLNET will be carried out by commitees that consist of nodes of the network. For each action a single node will be the main responsible. Beside the actions described in section 3 above, new actions can be proposed by nodes or groups of nodes. Two important tasks are defined in advance here and a budget is allocated to them: electronic communications and coordination. For other actions defined above an indicative budget is made. When an action involves financial support by the network, the node that undertakes an action presents an action plan and a budget to the management board. If this is approved, the node becomes responsible for this. The management board will meet two times a year. Meetings will be prepared and convened by the coordinator. To reduce travel costs, these meetings will be combined when possible with other events such as conferences or other activities of the network.

 

Rules for joining the network

An institute can join the network if shows evidence of a lasting interest in the Machine Learning and related fields, contributions to these fields and the intention to contribute to the goals of the network. A group or institute that wants to join MLNET-II can apply. The application will be evaluated by the management board and on the basis of this the management board will vote. Since the management board will not meet frequently, in general this will be handled by email. Eligibility for funding will of course be subject to EEC regulations.

 

Rules for removing nodes from the network

Nodes that do not contribute to the goals of the network and that show strongly reduced activity on the areas of the network can be removed. Once a year the management board makes a brief evaluation to check if any node satisfies these criteria. At any time a node can withdraw from the network.

 

Rules for becoming a main node

The management board can invite a node to become a main node.

 

Rules for committees

Some activities that are of key importance have been assigned to particular nodes:

A1: Universiteit van Amsterdam

A2: GMD

A3: Universita di Torino

The coordinator and the management board assign each of the other actions mentioned above to a node. Nodes can propose new actions of the network. Such a proposal involves a workplan, participants, results and a budget (if necessary). Substantial activities will be evaluated by the management board and minor activities by the coordinator.

Progress on an action will be monitored by two members of the management board (who do not participate in the action) and the coordinator. If not enough progress is made, this is reported to the management board that can decide to withdraw funding and assign the action to a new committee.

 

 

 

6.2. CLUSTER Computational Intelligence and Learning

 

The cluster actions will be managed by a management board that consists of one representative of each participating network. Each action of the cluster will be assigned to a node in one of the participating networks. This node will be responsible for performing the action and producing the required result. New actions can be proposed to the management board by any node of one of the networks. Budgets for activities will be allocated as specified above. The management board will meet twice a year, when possible in the context of relevant event.

The management board of the CLUSTER Computational Intelligence and Learning actions will consist of prof. Zimmerman (ERUDIT), prof. Fogarty (EvoNet), prof. Tervor Clarkson / Dr. Mark Plumbley (NEuroNet) and Drs. M. van Someren (MLNET)

For the following actions nodes and team leaders have been appointed:

C1: Technological Roadmap: ELITE (Prof. Zimmerman)

C2: Workshops and visits: this will be managed by the management board. Proposals can be submitted to the coordinator. Every three months, the management board takes decisions about these, during a management board meeting or by email

C3: Competition: this will be managed by KCL (Mark Plumbley)

C4: Teaching materials: this will be managed by (Universiteit van Amsterdam OR Universitat Dortmund)

C5:a2

Communication: this will be distributed over the nodes in the participating networks that are in charge of (electronic) communication apart from one node (Napier Univ., prof. Fogarty) that will be responsible for the CLUSTER specific communication.

 

 

7. Report

 

 

7.1. MLNET

 

At the end of the first year and of the second year, a management report will be written. For the activities above reports and other deliverables have been specified. The project has a duration f two years. We give the actions and deliverables for the first year and the second year. The nature of these is specified in the description of the actions. We summarise this:

Year 1

 

Action

Result

Responsible

Deadline

(month)

       

Annual report - 1

document

UvA

13

Online Information Services

Extended ML software library

GMD

11

 

Structured basic ML bibliography

 

11

 

Updated research database

 

11

 

Page with links to students projects

 

11

 

Page with teaching materials

 

11

Technological Roadmap- 1

document

Universita di Torino

11

workshops / visits

workshop / visit reports

management board / to be

decided

 

Application guide

document

NN

11

Education

teaching materials; reports

NN

11

       
       

 

 

 

Year 2

 

Action

Result

Responsible

Dead-line

       

Annual report - 2

document

UvA

24

Online Information Services

Extended ML software library

GMD

24

 

Structured basic ML bibliography

 

24

 

Updated research database

 

24

 

Page with links to students projects

 

24

 

Page with teaching materials

 

24

Technological Roadmap- 2

document

Universita di Torino

23

Workshops

reports

management board / to be

decided

 

Application guide - 2

document

NN

23

Education

teaching materials; reports

Dortmund

23

 

 

 

 

7.2. CLUSTER Computational Intelligence and Learning

After each year a management report wll be produced by the coordinator. The reports on other activities are described above at the descriptions of the activities. Here we give an overview. This overview applies to both years.

C1 Technological Roadmap

document

C2 Workshops and visits

reports

C3 Benchmarking competitions

reports on workshops

C4 Tutoring materials

tutoring materials

C5 Communication

WWW infrastructure and materials

C6 Coordination

much paper; management report

 

 

 

8. Duration and Resources

 

 

8.1. MLNET

 

The Network of Excellence will be active for 24 months. The budget of MLNET will be (figures in KECUs):

 

Task name

month

1-12

month

13-24

split by cost category per annum

TOTALS

A1

Coordination (administration, annual report, general management, management board travel)

45

45

pers: 20

overhead: 5

netw: 15

other: 5

90

A2

Online information services

30

30

pers: 25

overhead: 5

60

A3

Technological roadmap

(1 meeting; 1 week labour / year)

20

20

Pers: 5

Netw: 15

40

A4

workshops / meetings

50

50

netw: 50

100

A5

Application guide

(1 meeting; 1 week labour / year)

15

15

Pers: 5

Netw: 10

30

A6

Ambassadors

5

5

netw: 5

10

A7

Education support

10

10

Pers: 10

20

 

TOTALS

175

175

 

350

 

Tasks A1 will be carried out by the coordinator, Universiteit van Amsterdam. Task A2 will be carried out by GMD (Germany). Tasks A3, A4 and A7 will be delegated to nodes of MLNET.

 

 

8.2. CLUSTER Computational Intelligence and Learning

 

 

Task

month

1-12

month

13-24

Breakdown per annum

TOTALS

C1

Technological roadmap

30

30

Personnel: 10

Networking: 20

60

C2

Workshops /visits

45

45

Networking: 45

90

C3

Benchmarking competition

20

20

Personnel: 8

Networking: 12

40

C4

Education

5

5

Personnel: 5

10

C5

Communication

15

15

Personnel: 15

30

C6

Coordination + Management board travel

35

35

Personnel: 13

Overhead: 2

Networking: 20

70

 

TOTALS

150

 

150

300

 

 

 

The following nodes applied at the time the proposal for the MLNET network was sent to the CEU but did not sent their administrative data in time for the finalisation of the network programme:

Logic Programming Associates (UK)

Electricite de France (France)

Politecnico de Milano (Italy)

CSELT (Italy)

Katholieke Universiteit Brabant (Netherlands)

Vrije Universiteit Brussel (Belgium)

ISoft (France)

Imperial Cancer Research Fund (UK)

 

To date the nodes in ERUDIT, EvoNet and NEuroNet are those listed below.


'Dunarea de Jos' Galati University
Automatic Control and Electronics

Romania

Abo Akademi University
Institute for Advanced Management Systems Research

Finland

Advanced and Applied Technologies Institute (AATI)

Switzerland

AEROSPATIALE
A/BTE/EG/PERF/Avions

France

AICIA
System Engineering and Automation

Spain

Al Akhawayn University in Ifrane (AUI)
Division of Computer Science & Math, School of Science & Engineering

Morocco

Alcatel Telecom
Research Division

Germany

Algotech Sistemi S.r.l.

Italy

Amirkabik University of Technology
Department of Electrical Engineering

Iran

Ansaldo Ricerche s.r.l.
Electronics and Control

Italy

Arcelik A.S.
Research & Development Center

Turkey

Ariadne Explorations AB

Sweden

Augusta Technology Limited

United Kingdom

Bauman Moscow State Technical University
CIM Department

Russia

Bogazici University
Department of Computer Engineering

Turkey

Bosphorus University
Mathematics

Turkey

Brunel University
Department of Electrical Engineering and Electronics

United Kingdom

BT Newtorks and Systems - PIC
Advanced Apllications & Technologies

United Kingdom

Building Research Establishment
IT Research and Application

United Kingdom

Bulgarian Academy of Science - Institute of Computer and Communication Systems- Bulgaria Academy of Sciences
Intelligent Computer Technologies

Bulgaria

Carinthian Institute for Soft Computing Technologies - CISC

Austria

CARITRO - Cassa di Risparmio di Trento e Rovereto SpA

Italy

CAT - Computer Automation Technik GesmbH

Austria

Catholic University
Mathematical Department

Italy

Catholic University of Leuven
Department of Applied Economic Sciences, Information Systems Group

Belgium

CENTRAL RESEARCH FIAT -CRF -
HUMAN RESOURCES

Italy

Cerberus AG
Detektionstechnik

Switzerland

Codec S.R.L.
Research

Romania

Companhia de Celulose do Caima, S.A.
Research and Data Analysis Department

Portugal

Computer Center of the Russian Academy of Sciences
Artificial Intelligence Problems

Russia

Consiglio Nazionale delle Ricerche
IRSIP - Complex Systems Group

Italy

Consiglio Nazionale delle Ricerche
Istituto per le Tecnologie Informatiche Multimediali

Italy

Coventry University
Mathematics

United Kingdom

Cruse Leppelmann Kognitionstechnik GmbH
Image Analysis, Data Analysis, Simulation

Germany

CSEM - Centre Suisse d'Electronique et de Microtechnique SA
Industrial Control Section

Switzerland

DAEDALUS Informatics Ltd
R&D

Greece

Danieli Spa
R&D Department - Centro Ricerche Danieli

Italy

Danish Technological Institute
Centre for Biotechnologi

Denmark

De Montfort University
School of Computing Sciences

United Kingdom

DE-VI
DCP

Denmark

Defence Research Agency Malvern - DERA
CIS5

United Kingdom

Defence Research Agency Malvern - DERA
Technology for Decision Support

United Kingdom

Defence Research Establishment
Information System Technology

Sweden

Delft University of Technology
Dept. of Electrical Eng., Control Laboratory

The Netherlands

Dublin City University
School of Electronic Engineering. Control Systems Group

Ireland

E3i - Ecole d'Ingenieurs en Informatique pour l'Industrie
Computer Science

France

Ecole Nationale Superieure des Telecommunications
Departement Images

France

Efficiency

Czech Republic

EGE University School of Medicine
Biophysics

Turkey

ELBAB
Technology Management

Italy

ELITE Foundation - European Laboratory for Intelleigent Techniques Engineering

Germany

ELSAMPROJEKT A/S

Denmark

ENSAIT
Productique Textile

France

ENSEEIHT
LEEI

France

ERA Technology
Image & Advanced Processing

United Kingdom

Ericsson
Oy L M Ericsson Ab

Finland

EVIS Technologies GmbH

Austria

FLS Automation A/S

Denmark

Foundation for Research and Technology-Hellas (FORTH)
Institute of Computer Science (ICS)

Greece

FRAMATOME S.A.
Systèmes d'Information

France

Fraunhofer Institute for Solid State Technology
Microsystems

Germany

Fraunhofer-Institut IPA
Informationstechnik 621

Germany

Fuzzy Demonstrations-Zentrum Dortmund im Informatik Centrum Dortmund ICD e. V.

Germany

GEC Marconi Materials Technology Limited

United Kingdom

GFaI - Gesellschaft zur Förderung angewandter Informatik e.V.
Fuzzy Group

Germany

GMD-Forschungszentrum Informationstechnik Gmbh
Institute for System Design technology (SET)

Germany

HASSANE II UNIVERSITY MOHAMMEDIA
COMPUTER SCIENCES

MOROCCO

Hellenic Consultants SA
Department of Quantitatives Methods

Greece

Helsinki University of Technology
Control Engineering Laboratory

Finland

Heriot-Watt University
Intelligent Systems Laboratory

Scotland, UK

Heusch Boesefeldt GmbH
Verkehrsleittechnik/Content Providing

Germany

Higher Technical Institute (HTI)
Department of Mechanical Engineering

Cyprus

Hochschule Wismar
Fachbereich Elektrotechnik und Informatik

Germany

HTWK - Hochschule für Technik, Wirtschaft und Kultur
MSR - Measurement & Control Systems Group

Germany

Hugin Expert A/S

Denmark

I.S.E. Ingegneria dei Sistemi Elettronici s.r.l.
R&D

Italy

illycaffe s.p.a.
R&D

Italy

Imperial College of Science, Technology and Medicine
Department of Electrical & Electronic Engineering

United Kingdom

Industrial Insurance Company Ltd (Sampo Group)
Client Services Division

Finland

INSERM U 436
Department de Biostatistique et Informatique Medicale

France

Institut National des Sciences
LAAS/CNRS - Department de Genie Electrique

France

Institut Supérieur de Gestion de Tunis
Quantitative Methods

Tunisia

Institute of Electro-Mechanical Systems
Neuro-Fuzzy-Genetic Intelligent Control Research Center

Yugoslavia

Institute of Mathematics and Informatics
Optimization

Lithuania

Institute of Philosophy and Law of the National Academy of Sciences of Belarus
Department of Logic and Methodology of Scientific Cognition

Republic of Belarus

Instituto de Automatica Industrial-Consejo Superior de Investigaciones Cientificas
Sistemas

Spain

Intelligent Applications Ltd.

United Kingdom

Intrasoft SA
Products Development

Greece

IPCTI - Institute for Problems of Computers Technology and Informatization
Group of Neurocognitive Systems

Russia

IRSID
Computer Science / Artifcial Intelligence Department (IIA)

FRANCE

Istanbul Teknichal University
Management Engineering Department

Turkey

Istituto Universitario Navale di Napoli
Institute of Statistics and Mathematics/Faculty of Economics

Italy

Jordan University Of Science & Technology
Department of Mechanical Engineering

Jordan

Kangnam university
Electronics Engineering

south KOREA

Kazan State Technological University
Department of Informatics and Applied Mathematics

Russia

Kingston University
Department of Mathematics

United Kingdom

LABEIN
Information Technologies

Spain

Lappeenranta University of Technology
Department of Business Administration

Finland

Lappeenranta University of Technology
Information Technology

Finland

LIEG (Economic Managerial Engineering Laboratory DSEA (Electric System and Automation)
LIEG/DSEA Faculty of Engineering - University of Pisa

Italy

LYNX
Biomedical Application

Italy

Macs Tech Srl

Italy

MATRA Sytemes and Information
Research and Development

France

Max Planck Institute for Computer Science
Group : Programming Logics - Subgroup : Uncertain Reasoning

Germany

Medizinische Universität zu Lübeck
Institut für Technische Informatik

Germany

MicKS Meß-,Steuer- und Regelsysteme GmbH

Germany

Middle East Technical University
Department of Computer Engineering

Turkey

Middle East Technical University
Electrical Electronic Engineering

Turkey

Middle East Technical University
Mechanical Engineering Department

Turkey

Middle East Technical University
Mechanical Engineering Derpartment - Integrated Manufacturing Technologies Research Group(IMTRG)

Turkey

MIET(TU) - Moscow Institute of Electronic Technology (Technical University)
Intelligent Technologies Laboratory

Russia

Military Technical College
Department of Mathematical Engineering

Egypt

MIT - Management Intelligenter Technologien GmbH
Data Analysis

Germany

Moscow State Academy of Instrument Making and Informatics
Department of Applied Mathematics

Russia

Moscow State University
Department of Mathematics and Mechanics

Russia

National Cheng-Kung University
Department of Environmental Engineering

Taiwan, R.O.C.

National Institute of Electricity and Electronics
Department of Control and Computer Science

Algeria

National Technical University of Athens
Department of Chemical Engineering / Sector II - Laboratory of Automatic Process Control & Informatics

Greece

National Technical University of Athens
Intelligent Robotics and Automation Laboratory

Greece

Nuclear Research Centre (SCK.CEN)
Fuel Research Unit

Belgium

OK Partners Oy

Finland

Ostfold Research Foundation, STO
Institute for Information Technology

Norway

Otto-von Guericke-Universität Magdeburg
Faculty of Computer Science

Germany

Oxford Brookes University
School of Computing and Mathematical Sciences

United Kingdom

Parsytec Computer GmbH
Test Equipment

Germany

PetroStudies HB

Sweden

Philadelphia College of Textiles and Science
Computer Science Department

USA

Politechnica University of Bucharest
Department of Intelligent Control and Bioengineering

Romania

Politecnico di Bari
DEE Dipartimento di Elettronica ed Elettrotecnica

Italy

Politecnico di Bari
Department of Architecture and Town Planning

Italy

Politecnico di Milano
Department of Computer Engineering and Information Sciences

Italy

Politecnico di Torino
Dipartimento di Automatica e Informatica

Italy

Politehnica - University of Timisoara
Department of Automation

Romania

Polytechnic Faculty of Mons
Mathematics and Operational Research

Belgium

Polytechnic University of Bucharest
Department of Automatization & Computers

Romania

Pouliadis and Associates N. Branch
Network and Communication Department

Greece

Queen Mary and Westfield College
Department of Electronic Engineering

United Kingdom

Roskilde University
Intelligent Systems Laboratory, Department of Computer Science

Denmark

RWTH HOSPITAL
ANATOMIE I

Germany

Schneider Electric
General Research

France

SELMA

Yugoslavia

Servicios Profesionales de Santander Ltda S.P.S. Ltda

Colombia

SGS-THOMSON Microelectronics
Corporate Advanced System Architectures Group

Italy

Siemens AG Austria
PSE (Program and System Engineering)

Austria

Silesian Techical Univ.
Institute of Electronics, Division of Biomedical Electronics

Poland

Simulantti Oy

Finland

SINTEF
Electronics and Cybernetics

Norway

Slovak Technical University
Department of Mathematics

Slovakia

SMARTECH
Industrial Automation

Egypt

SOFTECO SISMAT SpA
Research and Development

Italy

Spanish Council for Scientific Research (CSIC)
Institut d'Investigació en Intel.ligència Artificial (IIIA) (Research Institute for Artificial Intelligence)

Spain

St. Petersburg State University
Institute for High-Performance Computing and Data Bases

Russia

SYCON Beratungs GmbH
Engineering

Germany

Syndesis Ltd

Greece

T&T - Teknoloji ve Tasarim Elektronik San Tic Ltd
R&D

Kadikoy - Istanbul

Taipale Engineering Ltd

Finland

Tarbiat Modarres University
Department of Electrical Engineering

Iran

Technical Univ.
Electrical Drives and Automation

Romania

Technical Univ.
Electrical Measurements and Materials

Romania

Technical University Darmstadt
Institute of Automatic Control

Germany

Technical University of Berlin
Electronics and Lighting Technology

Germany

Technical University of Budapest
Department of Telecommunications and Telematics

Hungary

Technical University of Cluj-Napoca
Department of Communications

Romania

Technical University of Denmark
Department of Automation

Denmark

Technical University of Iasi
Automatic Control and Industrial Informatics

Romania

Technical University of Riga
Specialized Institute of Intelligent Computer Technologies (SIICT)

Latvia

Technion - Israel Institut of Technology
Faculty of Industrial Engineering and Management

Israel

Technische Universitaet Wien
Institut fuer Informationssysteme (Nr. 184/2)

Austria

Technische Universität Clausthal
Institut für Technische Mechanik

Germany

Technische Universität Graz
Institut für Technische Informatik

Austria

Technische Universität Wien
Institut für Fertigungstechnik, Abteilung Austauschbau und Messtechnik

Austria

The Nottingham Trent University
Department of Manufacturing Engineering

United Kingdom

The University of Alberta
Civil and Environmental Engineering

Canada

The University of Edinburgh
Artificial Intelligence

United Kingdom

The University of the Gaziantep
Department of Mathematics

Turkey

Thomson-CSF
Central Research Laboratory

France

Tokyo Institute of Technology

Japan

TransferTech GmbH

Germany

TU Berlin
Institute of Process and Systems Engineering

Germany

TU Clausthal
Institut für Informatik

Germany

Ulyanovsk State Technical University
Computer Science Department

Russia

Ulyanovsk State Technical University
Computer Science Department

Russia

UMIST - University of Manchester - Institute of Science and Technology
Control Systems Centre

United Kingdom

Univ. of Rome
Department of Electronic Engineering

Italy

Universidad de Antioquia
Ingenieria Electronica

Colombia

Universidad de Valladolid
Economia y Admon. de Empresas

Spain

Universidad Pais Vasco, Facultad Informatica
Department of Computer Science and Artificial Intelligence

Spain

Universidade Nova Lisboa - FCT
Department of Computer Science

Portugal

Universita' di Genova
Department of Physics

Italy

Universita' LA SAPIENZA - Roma
Metodi e Modelli Matematici

Italy

Universita` di Catania
Department of Mathematics

Italy

Universitaet Kiel
Institut fuer Angewandte Physik

Germany

Universitaet-GH Paderborn
Abt. Soest, Fachbereich 16

Germany

Universitat de Girona
Electronica, Informatica i Automatica

Catalonia

Universität Leipzig
Institut für Logik und Wissenschaftstheorie

Germany

Universität Stuttgart Staatliche Materialprüfungsanstalt MPA
Warmfestigkeit - Expert Systems Group

Germany

Universite du Littoral
L.A.S.L (ex L.I.S.I.R)

France

Université Henri Poincaré
Centre de Recherche en Automatique de Nancy - CNRS-URA 821 / Faculté des Sciences

France

Universite Libre de Bruxelles
IRIDIA

Belgium

Université Paris II
Department of Informatics

France

Université Paul Sabatier
Institut de Recherche en Informatique de Toulouse (IRIT) / Equipe

France

University of Bologna
Physics Department

Italy

University of Bucharest
Department of Computer Science / Faculty of Mathematics

Romania

University of Cyprus
Department of Computer Science

Cyprus

University of Dortmund
Computer Science

Germany

University of Dortmund
Computer Science I

Germany

University of Ferrara
Department of Engineering

Italy

University of Fribourg
Institute of Informatics

Switzerland

University of Gent
Department of Applied Mathematics and Computer Science

Belgium

University of Granada
DECSAI - Department of Computer Science and Artificial Intelligence

Spain

University of Helsinki
Department of Economics and Management

Finland

University of Jyvaskyla
Department of Mathematics

Finland

University of Liege
Institute of Mathematics

Belgium

University of Macedonia
Department of Applied Informatics

Greece

University of Napoli Federico II
Department of Computer Science and Systems

Italy

University of Nebraska-Lincoln
Department of Biological Systems Engineering

USA

University of Ostrava
Institute for Research and Applications of Fuzzy Modeling

Czech Republic

University of Oulu
Department of Process Engineering, Control Engineering Laboratory

Finland

University of Paris Dauphine
LAMSADE

France

University of Paris VI
Laboratoire Informatique de Paris - LIP 6

France

University of Perpignan
I.M.P. Groupe Automatique, C.N.R.S.

France

University of Piraeus
Department of Industrial Management

Greece

University of Plymouth
Plymouth Engineering Design Centre

UK

University of Rome III
Departimento di Informatica e Automazione

Italy

University of Salerno
Faculty of Engineering

Italy

University of Sao Paulo
Department of Computer Science

Brazil

University of Savoie
LAMII / CESALP

France

University of Sheffield
Department of Automatic Control & Systems Engineering

United Kingdom

University of Southampton, Image, Speech and Intelligent Systems (ISIS) research group
Electronics and Computer Science

United Kingdom

University of Strathclyde
Department of Chemical Engineering, James Weir Bldg.

Scotland, United Kingdom

University of Technology, Vienna
Statistics and Pobability Theory

Austria

University of Teesside
School of Science and Technology

United Kingdom

University of Timisoara
POWER ENGINEERING

Romania

University of Ulster at Jordanstown
School of Information and Software Engineering

United Kingdom

University Paul Sabatier
CNRS - Laboratoire de Génie Eléctrique

France

University Victor Segalen Bordeaux II
Cognitive Sciences

France

UPM - Universidad Politecnica de Madrid
DISAM

Spain

Uppsala University
Institute of Earth Sciences /pal.

Sweden

Vinnitsa State Technical University
Computer Management

Ukraine

VSB - Technical University Ostrava
Department of Measurement and Control (DMC)

Czech Republic

VTT Automation
Machine Automation

Finland

VTT Technical Research Centre of Finland
Information Technology

Finland

Warsaw University
Institute of Mathematics

Poland

Warsaw University of Technology
Environmental Engineering

Poland

Warsaw University of Technology
Civil Engineering Faculty, Computer Methods Center

Poland

 

 

Nodes of EvoNet

 

ATLAN-tec KG

British Aerospace British Telecom Plc Cap Gemini Nederland BV CNET Daimler-Benz AG Danet GmbH Dassault Aviation

Estudio Atlas SL

EVIS Technologies GmbH

Fraunhofer Society

Genetica - Advanced Software Architectures srl

Hewlett-Packard Labs

Hollandse Signaalapparaten BV

Infoware Informatik Technologien GmbH

Intelligent Applications Ltd

National Aerospace Laboratory (NLR)

Parsytech Eastern Europe

Quadstone Ltd

Rolls Royce plc

Rover Group Ltd

Sentient Machine Research BV

SGS-Thomson Microelectronics

Siemens AG

Space Engineering SpA

Tensing GeoInformatica

 

 

Main Academic Nodes

 

Ecole Normale Superieure

Ecole Polytechnique

Napier University

Politecnico di Milano

Technische Universitaet Berlin

University of Dortmund

University of Granada (Department of Computer Science & Artificial Intelligence)

University of Granada (Department of Electronics and Computer Technology)

University of Sussex

University of the West of England

University of Vaasa

Vrije Universiteit Brussel

 

 

Associate Academic Nodes

 

Aarhus University

Aristotle University of Thessaloniki

Ashikaga Inst of Technology

Christian-Albrechts University of Kiel

Coventry University

De Montfort University

Ecole Nationale de l'Aviation Civile

Ecole Polytechnique Fédérale de Lausanne (EPFL)

EERIE-EMA

Foundation for Research and Technology (FORTH)

Gesellschaft zur Foerderung angewandter Informatik (GFaI)

GMD

Hellenic Complex Systems Laboratory

INRIA

Institut d'Informatique et de Mathématiques Applicquées de Grenoble (Institut IMAG)

Institut Francais du Petrole (IFP)

Institute for Research on Parallel Information Systems - National Research Council of Italy (IRSIP-CNR)

Jozef Stefan Institute

Leiden University

National Research Council of Italy (CNR-IRSIP)

Nottingham University

Politecnico di Torino

Technical University of Szczecin

Universidad de Malaga

Universidad Pontificia Comillas

Universidade do Algarve

Universita' di Milano

Universita' di Parma

Université de Tours

Université du Littoral

Université Libre de Bruxelles

University of Goettingen

University of Birmingham

University of East Anglia

University of Edinburgh

University of Essex

University of Exeter

University of Glasgow

University of Illinois at Urbana-Champaign

University of Lasi

University of Liverpool

University of Mannheim

University of Oxford

University of Plymouth

University of Reading

University of Sheffield

University of Southampton

University of the Basque Country

University of Wales

 

 

 

 

NEuroNet main nodes

 

  1. Kingís College London
  2.  

  3. ASOCIACIÓN PARA EL DESARROLLO DÉ LA INGENIERIA DEL CONOCIMIENTO -- INSTITUTO DE INGERIA DEL CONOCIMIENTO
  4.  

  5. UNIVERSITÀ DEGLI STUDI DI GENOVA
  6.  

  7. Teknillinen Korkeakoulou - Tekniska Hogskolan
  8.  

  9. INSTITUTE OF COMMUNICATIONS AND COMPUTERS, National Technical University of Athens
  10.  

  11. MIT Management Intelligenter Technologien
  12.  

  13. Oesterreichisches Forschungsinstitut fuer Artificial Intelligenz
  14.  

  15. RISØ NATIONAL LABORATORY
  16.  

  17. SENTIENT MACHINE RESEARCH
  18.  

  19. STICHTING NEURALE NETWERKEN (SNN), FOUNDATION OF NEURAL NETWORKS
  20.  

  21. INSTITUTS INTERNATIONAUX DE PHYSIQUE ET DE CHIMIE fondés par E.Solvay A.S.B.L.
  22.  

  23. Rheinische Friedrich-Wilhelms-Universitaet Bonn
  24.  

  25. UNIVERSITE PIERRE ET MARIE CURIE