Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

INFLUENCE

Financiado

Influence based Decision making in Uncertain Environments

Decision-theoretic sequential decision making (SDM) is concerned with endowing an intelligent agent with the capability to choose actions that optimize task performance. SDM techniques have the potential to revolutionize many aspe... ver más

31/01/2023

TU Delft

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

TECHNISCHE UNIVERSITEIT DELFT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-01-31

ERC-2017-STG: ERC Starting Grant

I+D

Cerrada hace 8 años

0% 100% 100%

3 Participantes

TU Delft

1.48M€ | Lider

NEW TECHNOLOGIES GLOBAL SYST...

169.92K€ | Participante

THE UNIVERSITY OF LIVERPOOL

N.D. | Participante

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Proyectos interesantes

PID2019-108141GB-I00 APRENDIZAJE Y PLANIFICACION CONTINUA 128K€ Cerrado

FJCI-2016-29820 Inteligencia Artificial - Optimización en sistemas multiagen... 50K€ Cerrado

TIN2012-31740 SELECCION DE ACCIONES BASADAS EN MODELOS PARA AMBIENTES DINA... 26K€ Cerrado

JCI-2008-2619 Argumentation and Learning in Multi-Agent Systems 101K€ Cerrado

PID2020-112754GB-I00 MODELIZACION Y ANALISIS DE CONTEXTOS PARA EL DISEÑO DE SISTE... 58K€ Cerrado

TIN2017-88476-C2-1-R RECONOCIMIENTO DE ACTIVIDADES Y PLANIFICACION AUTOMATICA PAR... 174K€ Cerrado

Duración del proyecto: 62 meses Fecha Inicio: 2017-11-16
Fecha Fin: 2023-01-31

Líder del proyecto

TECHNISCHE UNIVERSITEIT DELFT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto Decision-theoretic sequential decision making (SDM) is concerned with endowing an intelligent agent with the capability to choose actions that optimize task performance. SDM techniques have the potential to revolutionize many aspects of society and recent successes, e.g., agents that play Atari games and beat a world champion in the game of Go, have sparked renewed interest in this field. However, despite these successes, fundamental problems of scalability prevents these methods from addressing other problems with hundreds or thousands of state variables. For instance, there is no principled way of computing an optimal or near-optimal traffic light control plan for an intersection that takes into account the current state of traffic in an entire city. I will develop one in this project. To achieve this, I will develop a new class of influence-based SDM methods that overcome scalability issues for such problems by using novel ways of abstraction. Considered from a decentralized system perspective, the intersection’s local problem is manageable, but the influence that the rest of the network exerts on it is complex. The key idea is that by using (deep) machine learning methods, we can learn sufficiently accurate representations of such influence to facilitate near-optimal decisions. This project will construct a theoretical framework for such approximate influence representations and SDM methods that use them. Scalability of these methods will be demonstrated by rigorous empirical evaluation on two simulated challenge domains: traffic lights control in an entire city, and robotic order picking in a large-scale autonomous warehouse. If successful, INFLUENCE will produce a range of influence-based SDM algorithms that can, in a principled manner, deal with a broad range of very large complex problems consisting of hundreds or thousands of variables, thus making an important step towards realizing the promise of autonomous agent technology.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores