Jacob W. Crandall

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Michael A. GoodrichComputer Science Department, Brigham Young UniversityVerified email at cs.byu.edu
M. CummingsProfessor, Duke UniversityVerified email at duke.edu
Iyad RahwanCenter for Humans & Machines, Max Planck Institute for Human DevelopmentVerified email at mpib-berlin.mpg.de
Jean-François BonnefonToulouse School of Economics - TSMR - IAST - CNRS - ANITIVerified email at tse-fr.eu
Carl NehmeVerified email at mit.edu
Manuel CebrianUniversidad Carlos III de MadridVerified email at uc3m.es
Azim ShariffAssociate Professor; University of British ColumbiaVerified email at psych.ubc.ca
Mayada OudahNew York University Abu DhabiVerified email at nyu.edu
Sherief AbdallahBritish University in DubaiVerified email at buid.ac.ae
Subramanian RamamoorthyProfessor of Robot Learning and Autonomy, School of Informatics, University of EdinburghVerified email at ed.ac.uk
Stefano V. AlbrechtSchool of Informatics, University of EdinburghVerified email at ed.ac.uk
Thomas J. PalmerVerified email at tjpalmer.com
Wen ShenVerified email at uci.edu
Tim McLainProfessor of Mechanical Engineering, Brigham Young UniversityVerified email at byu.edu
Cristina Videira LopesInformation and Computer Sciences, University of California, IrvineVerified email at ics.uci.edu
Emilia Barakovaassistant professor, Eindhoven University of TechnologyVerified email at tue.nl
Zaid AlmahmoudPhD Student, Birkbeck, University of LondonVerified email at student.bbk.ac.uk
Morgan QuigleyOpen Source Robotics FoundationVerified email at osrfoundation.org
Maria GiniProfessor of Computer Science and Engineering, University of MinnesotaVerified email at umn.edu
Nicholas JohnsonTarget, University of MinnesotaVerified email at cs.umn.edu

Jacob W. Crandall

Professor of Computer Science, Brigham Young University

Verified email at cs.byu.edu - Homepage

Artificial intelligence human-machine interaction multi-agent systems machine learning game theory


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Machine behaviour I Rahwan, M Cebrian, N Obradovich, J Bongard, JF Bonnefon, C Breazeal, ... Nature 568 (7753), 477-486, 2019	613	2019
Validating human-robot interaction schemes in multitasking environments JW Crandall, MA Goodrich, DR Olsen, CW Nielsen IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and …, 2005	326	2005
Experiments in adjustable autonomy MA Goodrich, DR Olsen, JW Crandall, TJ Palmer Proceedings of IJCAI Workshop on autonomy, delegation and control …, 2001	291	2001
Characterizing efficiency of human robot interaction: A case study of shared-control teleoperation JW Crandall, MA Goodrich IEEE/RSJ international conference on intelligent robots and systems 2, 1290-1295, 2002	187	2002
Cooperating with machines JW Crandall, M Oudah, F Ishowo-Oloko, S Abdallah, JF Bonnefon, ... Nature communications 9 (1), 1-12, 2018	160	2018
Managing autonomy in robot teams: observations from four experiments MA Goodrich, TW McLain, JD Anderson, J Sun, JW Crandall 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI …, 2007	142	2007
Developing performance metrics for the supervisory control of multiple robots JW Crandall, ML Cummings Proceedings of the ACM/IEEE international conference on Human-robot …, 2007	112	2007
Predicting operator capacity for supervisory control of multiple UAVs ML Cummings, CE Nehme, J Crandall, P Mitchell Innovations in Intelligent Machines-1, 11-37, 2007	93	2007
Identifying predictive metrics for supervisory control of multiple robots JW Crandall, ML Cummings IEEE Transactions on Robotics 23 (5), 942-951, 2007	84	2007
Experiments in adjustable autonomy JW Crandall, MA Goodrich 2001 IEEE International Conference on Systems, Man and Cybernetics. e …, 2001	80	2001
Teleoperation and beyond for assistive humanoid robots MA Goodrich, JW Crandall, E Barakova Reviews of Human factors and ergonomics 9 (1), 175-226, 2013	70	2013
Behavioural evidence for a transparency–efficiency tradeoff in human–machine cooperation F Ishowo-Oloko, JF Bonnefon, Z Soroye, J Crandall, I Rahwan, T Rahwan Nature Machine Intelligence 1 (11), 517-521, 2019	69	2019
Learning to compete, coordinate, and cooperate in repeated games using reinforcement learning JW Crandall, MA Goodrich Machine Learning 82 (3), 281-314, 2011	66	2011
Belief and truth in hypothesised behaviours SV Albrecht, JW Crandall, S Ramamoorthy Artificial Intelligence 235, 63-94, 2016	62	2016
Computing the effects of operator attention allocation in human control of multiple robots JW Crandall, ML Cummings, M Della Penna, PMA De Jong IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and …, 2010	59	2010
An operator function taxonomy for unmanned aerial vehicle missions CE Nehme, JW Crandall, ML Cummings 12th international command and control research and technology symposium, 19-21, 2007	58*	2007
Learning to compete, compromise, and cooperate in repeated general-sum games JW Crandall, MA Goodrich Proceedings of the 22nd international conference on Machine learning, 161-168, 2005	56	2005
Towards predicting robot team performance JW Crandall, CW Nielsen, MA Goodrich SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems …, 2003	49	2003
Neglect tolerant teaming: Issues and dilemmas MA Goodrich, JW Crandall, JL Stimpson Proceedings of the 2003 AAAI Spring Symposium on Human Interaction with …, 2003	45	2003
Identifying generalizable metric classes to evaluate human-robot teams P Pina, M Cummings, J Crandall, M Della Penna Proc. 3rd Ann. Conf. Human-Robot Interaction, 13-20, 2008	42	2008

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