Jacob William Crandall

Artificial Intelligence · 2001–2019

A researcher in the Science corpus: 6 papers; trusts 21 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #23,853 of 35,345 by trust here, #18,586 by citations
• Reinforcement Learning — 41 citations into it
• Sociology and Political Science — 35 citations into it
• Management Science and Operations Research — 25 citations into it; #7,564 of 11,946 by trust here, #8,906 by citations
• Game & Decision Theory — 18 citations into it
• Economics and Econometrics — 15 citations into it
• Control and Systems Engineering — 6 citations into it; #4,959 of 8,257 by trust here, #5,784 by citations
• Optimization & Training — 5 citations into it

Whom Jacob William Crandall trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Michael L. Littman
2. R. May
3. Richard S. Sutton
4. Herbert Gintis
5. Brian Skyrms
6. Michael H. Bowling
7. Gavin Adrian Rummery
8. Ronen I. Brafman
9. Junling Hu
10. Amy Greenwald

Who trusts Jacob William Crandall overall

1. Pablo Hernandez-Leal
2. Guy Hoffman
3. Iyad Rahwan
4. Jean C. Scholtz
5. Michael Kaisers
6. Alex Peysakhovich
7. Cynthia Lynn Breazeal
8. Adam Lerer
9. Michael A. Goodrich
10. Tim Baarslag

Papers from Jacob William Crandall's perspective

Ordered by how strongly the researchers Jacob William Crandall trusts endorse each paper. Same corpus, a different order for every researcher.

1. Reinforcement Learning: An Introduction
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Human-level control through deep reinforcement learning
5. The Theory of Learning in Games
6. Reinforcement Learning: A Survey
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
10. Markov Games as a Framework for Multi-Agent Reinforcement Learning

Their papers

• Cooperating with machines (2017) — 251 citations
• Learning to compete, coordinate, and cooperate in repeated games using reinforcement learning (2011) — 54 citations
• Experiments in adjustable autonomy (2001) — 315 citations
• Machine behaviour (2019) — 1,247 citations
• Learning to Interact with a Human Partner (2015) — 21 citations
• Validating human-robot interaction schemes in multitasking environments (2005) — 298 citations