Craig Boutilier

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Tyler LuResearch Scientist, GoogleVerified email at cs.toronto.edu
Ronen BrafmanDepartment of Computer Science, Ben-Gurion UniversityVerified email at cs.bgu.ac.il
Pascal PoupartUniversity of WaterlooVerified email at uwaterloo.ca
Moises GoldszmidtDistinguished Engineer at AppleVerified email at apple.com
Holger HoosRWTH Aachen University, Germany • Leiden University, Netherlands • University of British ColumbiaVerified email at cs.rwth-aachen.de
Tuomas SandholmAngel Jordan University Professor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Jesse HoeyProfessor of Computer Science, University of WaterlooVerified email at cs.uwaterloo.ca
David PooleProfessor of Computer Science, University of British ColumbiaVerified email at cs.ubc.ca
Thomas DeanBrown UniversityVerified email at brown.edu
Alex MihailidisProfessor, Department of Occupational Science & Occupational Therapy, University of TorontoVerified email at utoronto.ca
Dale SchuurmansResearch Scientist, Google Brain / Professor, University of AlbertaVerified email at cs.ualberta.ca
Martin MladenovGoogle ResearchVerified email at google.com
Richard DeardenReader in Intelligent Robotics, University of BirminghamVerified email at cs.bham.ac.uk
David C. ParkesGeorge F. Colony Professor of Computer Science, Harvard UniversityVerified email at eecs.harvard.edu
Bob PriceResearch Scientist, Palo Alto Research CenterVerified email at parc.com
Paolo ViappianiCNRS researcher, LAMSADE, Université Paris DauphineVerified email at dauphine.psl.eu
Branislav KvetonAmazonVerified email at amazon.com
Georgios ChalkiadakisTechnical University of CreteVerified email at isc.tuc.gr
Carmel DomshlakProfessor, TechnionVerified email at ie.technion.ac.il
Steve HanksProfessor of Computer Science, University of WashingtonVerified email at cs.washington.edu

Craig Boutilier

Principal Scientist, Google

Verified email at google.com - Homepage

Artificial Intelligence Decision Theory Recommender Systems Computational Economics Reinforcement Learning


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The dynamics of reinforcement learning in cooperative multiagent systems C Claus, C Boutilier AAAI/IAAI 1998 (746-752), 2, 1998	1679	1998
Decision-theoretic planning: Structural assumptions and computational leverage C Boutilier, T Dean, S Hanks Journal of Artificial Intelligence Research 11, 1-94, 1999	1532	1999
CP-nets: A tool for representing and reasoning withconditional ceteris paribus preference statements C Boutilier, RI Brafman, C Domshlak, HH Hoos, D Poole Journal of artificial intelligence research 21, 135-191, 2004	1206	2004
Context-specific independence in Bayesian networks C Boutilier, N Friedman, M Goldszmidt, D Koller arXiv preprint arXiv:1302.3562, 2013	963	2013
Planning, learning and coordination in multiagent decision processes C Boutilier TARK 96, 195-210, 1996	666	1996
Stochastic dynamic programming with factored representations C Boutilier, R Dearden, M Goldszmidt Artificial intelligence 121 (1-2), 49-107, 2000	618	2000
SPUDD: Stochastic planning using decision diagrams J Hoey, R St-Aubin, A Hu, C Boutilier arXiv preprint arXiv:1301.6704, 2013	609	2013
Exploiting structure in policy construction C Boutilier, R Dearden, M Goldszmidt IJCAI 14, 1104-1113, 1995	570	1995
Sequential optimality and coordination in multiagent systems C Boutilier IJCAI 99, 478-485, 1999	492	1999
Toward a logic for qualitative decision theory C Boutilier Principles of knowledge representation and reasoning, 75-86, 1994	471	1994
Hierarchical solution of Markov decision processes using macro-actions M Hauskrecht, N Meuleau, LP Kaelbling, TL Dean, C Boutilier arXiv preprint arXiv:1301.7381, 2013	399	2013
Reasoning With Conditional Ceteris Paribus Preference Statements. C Boutilier, RI Brafman, HH Hoos, D Poole UAI 99, 71-80, 1999	362	1999
Solving combinatorial auctions using stochastic local search HH Hoos, C Boutilier Aaai/iaai, 22-29, 2000	345	2000
Symbolic dynamic programming for first-order MDPs C Boutilier, R Reiter, B Price IJCAI 1, 690-700, 2001	334	2001
A POMDP formulation of preference elicitation problems C Boutilier AAAI/IAAI, 239-246, 2002	333	2002
Conditional logics of normality: a modal approach C Boutilier Artificial intelligence 68 (1), 87-154, 1994	316	1994
Computing optimal policies for partially observable decision processes using compact representations C Boutilier, D Poole Proceedings of the National Conference on Artificial Intelligence, 1168-1175, 1996	314	1996
Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process J Hoey, P Poupart, A von Bertoldi, T Craig, C Boutilier, A Mihailidis Computer Vision and Image Understanding 114 (5), 503-519, 2010	312	2010
Decision-theoretic, high-level agent programming in the situation calculus C Boutilier, R Reiter, M Soutchanski, S Thrun AAAI/IAAI 2000, 355-362, 2000	288	2000
Solving very large weakly coupled Markov decision processes N Meuleau, M Hauskrecht, KE Kim, L Peshkin, LP Kaelbling, TL Dean, ... AAAI/IAAI, 165-172, 1998	278	1998

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