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Composing Learning for Artificial Cognitive Systems

Project description

Cognitive Systems and Robotics One of the aspirations of machine learning is to develop intelligent systems that can address a wide variety of control problems of many different types. Currently, the technology used to specify, solve and analyse one control problem typically cannot be reused on a different problem. The purpose of CompLACS is to develop a unified toolkit that will incorporate the most successful approaches to control problems within a single framework, including bandit problems, Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), continuous stochastic control, and multi-agent systems. The toolkit will also provide a generic interface to specifying problems and analysing performance, by mapping intuitive, human-understandable goals into machine-understandable objectives, and by mapping algorithm performance and get back into human-understandable terms.

 

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Coordinator

UNIVERSITY COLLEGE LONDON

Address

Gower Street
Wc1e 6bt London

United Kingdom

Activity type

Other

EU Contribution

€ 1 160 410

Administrative Contact

Greta Borg-Carbott (Ms.)

Participants (8)

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MONTANUNIVERSITAET LEOBEN

Austria

EU Contribution

€ 535 705

TECHNISCHE UNIVERSITAT BERLIN

Germany

EU Contribution

€ 321 468

MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV

Germany

EU Contribution

€ 148

TECHNISCHE UNIVERSITAT DARMSTADT

Germany

EU Contribution

€ 580 786

INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE

France

EU Contribution

€ 536 887

STICHTING KATHOLIEKE UNIVERSITEIT

Netherlands

EU Contribution

€ 533 628

ROYAL HOLLOWAY AND BEDFORD NEW COLLEGE

United Kingdom

EU Contribution

€ 321 413

UNIVERSITY OF BRISTOL

United Kingdom

EU Contribution

€ 436 839

Project information

Grant agreement ID: 270327

Status

Closed project

  • Start date

    1 March 2011

  • End date

    28 February 2015

Funded under:

FP7-ICT

  • Overall budget:

    € 5 853 582

  • EU contribution

    € 4 427 284

Coordinated by:

UNIVERSITY COLLEGE LONDON

United Kingdom