Efficient sequential decision making under uncertainty

Project Information

ESDEMUU

Grant agreement ID: 273816

Project closed

Start date 1 May 2011

End date 30 April 2013

Funded under

• Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)

Total cost € 232 777,80

EU contribution € 232 777,80

Coordinated by

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Switzerland

Objective

"Many applications require efficient methods for automated decision making, such as control systems, crisis response, finance, logistics, network security, robotics and traffic management. These problems involve sequential learning and decision making under uncertainty in an unknown environment. As we have incomplete information about the state and dynamics of the environment, the outcome of any specific plan is uncertain. Statistical decision theory offers a framework for finding optimal solutions, but in most problems of interest exact inference and planning are intractable.

The project will develop efficient approximate methods for nearly optimal learning and decision making in such problems. Our first goal is to obtain provably efficient algorithms for decision making in discrete, fully observable environments. Our second goal is to extend these to continuous and partially observable domains. Recent advances in statistical learning theory and in stochastic planning, make this avenue of research particularly promising. Our third theoretical goal is to consider collaborative planning among multiple agents in unknown environments for each of the above cases.

Finally, we shall develop open source code and perform extensive comparative experiments in classical benchmark problems for evaluation purposes. As a more realistic test-bed, we shall focus on the network intrusion detection and response problem, where we must safeguard a network against the attacks of malicious users.

The project coordinator is an expert on Bayesian reinforcement learning and stochastic planning and the host institution has produced seminal breakthroughs in the area of distributed planning, while both have prior experience in problems of network intrusion detection."

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.

• engineering and technology › electrical engineering, electronic engineering, information engineering › electronic engineering › control systems
• natural sciences › computer and information sciences › artificial intelligence › machine learning › reinforcement learning
• natural sciences › computer and information sciences › computer security › network security
• engineering and technology › electrical engineering, electronic engineering, information engineering › electronic engineering › robotics

You need to log in or register to use this function

Programme(s)

• FP7-PEOPLE - Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)

Topic(s)

• FP7-PEOPLE-2010-IEF - Marie-Curie Action: "Intra-European fellowships for career development"

Call for proposal

FP7-PEOPLE-2010-IEF
See other projects for this call

Funding Scheme

MC-IEF - Intra-European Fellowships (IEF)

Coordinator

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

• EU contribution: € 232 777,80
• Address: BATIMENT CE 3316 STATION 1 ; 1015 Lausanne ; Switzerland
• Region: Schweiz/Suisse/Svizzera › Région lémanique › Vaud
• Activity type: Higher or Secondary Education Establishments
• Total cost: No data