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ALIZ-E

Financiado

Cerrado

Adaptive Strategies for Sustainable Long Term Social Interaction

The goal of ALIZ-E is to develop methods for developing and testing interactive, mobile robots which will be able to interact with human users over extended periods of time, i.e. a possibly non-continuous succession of interaction... ver más

31/08/2014

UOP

11M€

Presupuesto del proyecto: 11M€

Líder del proyecto

UNIVERSITY OF PLYMOUTH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 12 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2014-08-31 No tenemos la información de la convocatoria

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

12 Participantes

UOP

0.00€ | Lider

ALDEBARAN

N.D. | Participante

FCSR

N.D. | Participante

CNR

N.D. | Participante

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Líder del proyecto

UNIVERSITY OF PLYMOUTH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 11M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The goal of ALIZ-E is to develop methods for developing and testing interactive, mobile robots which will be able to interact with human users over extended periods of time, i.e. a possibly non-continuous succession of interactions which can refer back to, and build forth on, previous experiences.To achieve this aim, ALIZ-E will address three related issues in developing interactive robots capable of self-sustaining medium- to long-term autonomous operation in real-world indoor environments. One, ALIZ-E will address how long-term experience can be acquired, to ground actions and interactions across time. Two, ALIZ-E will address how a system can deal robustly with inevitable differences in quality in perceiving and understanding a user and her environment. To this end, novel methods for adaptively controlling how a system invokes and adaptively balances a hybrid ensemble of processing and behaviours. Third, ALIZ-E will address how a system can adapt its interaction based on how user behaviour changes over time and contexts.To demonstrate and evaluate scientific methods, ALIZ-E will instantiate and evaluate these methods in working systems that interact with hospitalized children undergoing diabetes treatment. Long-term interaction in this context means interactions over a period of up to 5 days (possibly longer). Choosing this scenario, ALIZ-E makes it possible to bring existing extensive experience in conducting clinical trials of IT technology to the field of cognitive systems and human-robot interaction, to help develop novel methods for evaluating interactive robots at system-level.The theory and practice of ALIZ-E will impact on theoretical cognitive systems research (eg. memory, long-term affective interaction), implementation (eg. adaptive deployment of processing and behaviour for robust interaction, cloud computing for cognitive systems, speech processing for young users) and commercial applications of these technologies.

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