James MacGlashan
James MacGlashan
Verified email at cogitai.com
Cited by
Cited by
Interactive learning from policy-dependent human feedback
J MacGlashan, MK Ho, R Loftin, B Peng, D Roberts, ME Taylor, ...
arXiv preprint arXiv:1701.06049, 2017
Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning
R Loftin, B Peng, J MacGlashan, ML Littman, ME Taylor, J Huang, ...
Autonomous agents and multi-agent systems 30 (1), 30-59, 2016
Reinforcement Learning as a Framework for Ethical Decision Making.
D Abel, J MacGlashan, ML Littman
AAAI Workshop: AI, Ethics, and Society 16, 02, 2016
A strategy-aware technique for learning behaviors from discrete human feedback
R Loftin, J MacGlashan, ML Littman, ME Taylor, DL Roberts
North Carolina State University. Dept. of Computer Science, 2014
Showing versus doing: Teaching by demonstration
MK Ho, ML Littman, J MacGlashan, F Cushman, JL Austerweil
NeurIPS, 2016
Environment-independent task specifications via GLTL
ML Littman, U Topcu, J Fu, C Isbell, M Wen, J MacGlashan
arXiv preprint arXiv:1704.04341, 2017
Reducing errors in object-fetching interactions through social feedback
D Whitney, E Rosen, J MacGlashan, LLS Wong, S Tellex
2017 IEEE International Conference on Robotics and Automation (ICRA), 1006-1013, 2017
A need for speed: Adapting agent action speed to improve task learning from non-expert humans
B Peng, J MacGlashan, R Loftin, ML Littman, DL Roberts, ME Taylor
Proceedings of the International Joint Conference on Autonomous Agents and …, 2016
Interactive visual clustering
M Desjardins, J MacGlashan, J Ferraioli
Proceedings of the 12th international conference on Intelligent user …, 2007
Goal-based action priors
D Abel, DE Hershkowitz, G Barth-Maron, S Brawner, K O'Farrell, ...
Twenty-Fifth International Conference on Automated Planning and Scheduling, 2015
Grounding English Commands to Reward Functions.
J MacGlashan, M Babes-Vroman, Marie desJardins, ML Littman, ...
Robotics: Science and Systems, 2015
Social is special: A normative framework for teaching with and learning from evaluative feedback
MK Ho, J MacGlashan, ML Littman, F Cushman
Cognition 167, 91-106, 2017
Between imitation and intention learning
J MacGlashan, ML Littman
Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015
Planning with abstract markov decision processes
N Gopalan, M desJardins, ML Littman, J MacGlashan, S Squire, S Tellex, ...
ICAPS, 2017
Portable option discovery for automated learning transfer in object-oriented Markov decision processes
N Topin, N Haltmeyer, S Squire, J Winder, J MacGlashan
Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015
Learning something from nothing: Leveraging implicit human feedback strategies
R Loftin, B Peng, J MacGlashan, ML Littman, ME Taylor, J Huang, ...
The 23rd IEEE International Symposium on Robot and Human Interactive …, 2014
Training an agent to ground commands with reward and punishment
J MacGlashan, ML Littman, R Loftin, B Peng, DL Roberts, ME Taylor
Proceedings of the AAAI Machine Learning for Interactive Systems Workshop, 6-12, 2014
Brown-umbc reinforcement learning and planning (burlap)
J MacGlashan
Minecraft as an experimental world for AI in robotics
KC Aluru, S Tellex, J Oberlin, J MacGlashan
2015 aaai fall symposium series, 2015
Implementing the deep q-network
M Roderick, J MacGlashan, S Tellex
arXiv preprint arXiv:1711.07478, 2017
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