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Machine Learning-Robotics

Domaine
Machine Learning-Robotics
Domain - extra
Année
2010
Starting
as soon as possible
État
Open
Sujet
Monte-Carlo Tree Search (MCTS) is a revolution in high-dimensional planning.
This ph.D. is devoted to:
(i) improvement of MCTS algorithm;
(ii) experiments in the Iomca platform.
Thesis advisor
TEYTAUD Olivier
Co-advisors
Laboratory
Collaborations
NUTN (Taiwan)
Artelys (www.artelys.com)
Contact: olivier.teytaud à inria.fr
Abstract
The IOMCA project involves several partners and includes a platform for dynamic optimization.
- several artificial benchmarks will be included (some of them to be chosen/developed
by the ph.D. student)
- some real-world benchmarks developed by the company involved in the project (not to be
developped by the ph.D. student, but to be used in experiments).
- a MCTS implementation (to be developped and improved by the ph.D. student)
- other implementations (to be included in tests, but not developped by the ph.D. student)

The work will include tests on the very important (economically and ecologically) electricity production management problem.

Objectives:
- finding significant and generic improvements to the MCTS algorithm;
- testing MCTS versus other algorithms depending on the size of the problem; the energy
management problem is necessarily included in tests.

Contact: olivier.teytaud à inria.fr

Context
Main focus of MCTS = high-dimensional planning when no evaluation function is available.

Many successful applications in games (in particular the game of Go http://www.lri.fr/~teytaud/mogo.html), but also the important case of general game playing.

Some publications on difficult high-dimensional planning problems:
- industrial application published in ICML: http://hal.inria.fr/inria-00379523/
- application to active learning published in ECML: http://hal.inria.fr/inria-00433866/
- application to non-linear optimization in Algorithmica: http://hal.inria.fr/inria-00369788

Contact: olivier.teytaud à inria.fr
Objectives
Objectives:
- finding significant and generic improvements to the MCTS algorithm;
- testing MCTS versus other algorithms depending on the size of the problem; the energy
management problem is necessarily included in tests.
Work program
Understanding of the field of dynamic optimization.

Understanding of MCTS algorithms.

Writing of a state of the art.

Development of a MCTS implementation in the Iomca platform.

Finding new ideas for MCTS.

Comparing MCTS and other techniques (developped by other teams/persons) on artificial scalable benchmarks (developed by other teams/persons and/or by the ph.D. student).

Testing MCTS on energy management problems.
Extra information
Contact: olivier.teytaud à inria.fr
Prerequisite
Programming skills. C or java or C++ is ok.
Mathematical programming or statistics are a bonus.
Ability to work in a team.
Contact: olivier.teytaud à inria.fr
Détails
Expected funding
international ANR funding
Status of funding
Confirmed
Candidates
Utilisateur
Créé
Mercredi 10 mars 2010 11:11:00 CET
dernière modif.
Mercredi 10 mars 2010 11:12:34 CET

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Ecole Doctorale Informatique Paris-Sud


Directrice
Nicole Bidoit
Assistante
Stéphanie Druetta
Conseiller aux thèses
Dominique Gouyou-Beauchamps

ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr