yann chevaleyre
yann chevaleyre
Professeur d'intelligence artificielle, université Paris Dauphine PSL
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Issues in multiagent resource allocation
Y Chevaleyre, PE Dunne, U Endriss, J Lang, M Lemaitre, N Maudet, ...
A short introduction to computational social choice
Y Chevaleyre, U Endriss, J Lang, N Maudet
International conference on current trends in theory and practice of …, 2007
Theoretical analysis of the multi-agent patrolling problem
Y Chevaleyre
Proceedings. IEEE/WIC/ACM International Conference on Intelligent Agent …, 2004
Recent advances on multi-agent patrolling
A Almeida, G Ramalho, H Santana, P Tedesco, T Menezes, V Corruble, ...
Advances in Artificial Intelligence–SBIA 2004: 17th Brazilian Symposium on …, 2004
Fair Allocation of Indivisible Goods.
S Bouveret, Y Chevaleyre, N Maudet, H Moulin
Handbook of computational social choice, 284-310, 2016
Solving multiple-instance and multiple-part learning problems with decision trees and rule sets. Application to the mutagenesis problem
Y Chevaleyre, JD Zucker
Advances in Artificial Intelligence: 14th Biennial Conference of the …, 2001
Learning conditionally lexicographic preference relations
R Booth, Y Chevaleyre, J Lang, J Mengin, C Sombattheera
ECAI 2010, 269-274, 2010
Preference handling in combinatorial domains: From AI to social choice
Y Chevaleyre, U Endriss, J Lang, N Maudet
AI magazine 29 (4), 37-37, 2008
Reaching envy-free states in distributed negotiation settings
Y Chevaleyre, U Endriss, S Estivie, N Maudet
Multiagent resource allocation in k-additive domains: preference representation and complexity
Y Chevaleyre, U Endriss, S Estivie, N Maudet
Annals of Operations Research 163, 49-62, 2008
Distributed fair allocation of indivisible goods
Y Chevaleyre, U Endriss, N Maudet
Artificial Intelligence 242, 1-22, 2017
Multiagent resource allocation with k-additive utility functions
Y Chevaleyre, U Endriss, S Estivie, N Maudet
Proc. DIMACS-LAMSADE workshop on computer science and decision theory 3, 83-100, 2004
A theoretical analysis of multi-agent patrolling strategies
Y Chevaleyre, F Sempe, G Ramalho
AAMAS 4, 1524-1525, 2004
Possible winners when new candidates are added: The case of scoring rules
Y Chevaleyre, J Lang, N Maudet, J Monnot
Proceedings of the AAAI Conference on Artificial Intelligence 24 (1), 762-767, 2010
Expressive Power of Weighted Propositional Formulas for Cardinal Preference Modeling.
Y Chevaleyre, U Endriss, J Lang
Institute for Logic, Language and Computation (ILLC), University of Amsterdam, 2006
Representing utility functions via weighted goals
J Uckelman, Y Chevaleyre, U Endriss, J Lang
Mathematical Logic Quarterly 55 (4), 341-361, 2009
Randomization matters how to defend against strong adversarial attacks
R Pinot, R Ettedgui, G Rizk, Y Chevaleyre, J Atif
International Conference on Machine Learning, 7717-7727, 2020
Local envy-freeness in house allocation problems
A Beynier, Y Chevaleyre, L Gourvès, A Harutyunyan, J Lesca, N Maudet, ...
Autonomous Agents and Multi-Agent Systems 33, 591-627, 2019
Learning ordinal preferences on multiattribute domains: The case of CP-nets
Y Chevaleyre, F Koriche, J Lang, J Mengin, B Zanuttini
Preference learning, 273-296, 2010
New candidates welcome! Possible winners with respect to the addition of new candidates
Y Chevaleyre, J Lang, N Maudet, J Monnot, L Xia
Mathematical Social Sciences 64 (1), 74-88, 2012
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