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Inverse reinforcement learning (IRL) attempts to infer human rewards or preferences from observed behavior.
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Andrew Ng and Stuart Russell · 2000
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Richard E Nisbett, Kaiping Peng, Incheol Choi, and Ara Norenzayan · 2001
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Cristian Calude · 2002
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The speed prior: a new simplicity measure yielding near-optimal computable predictions
Jürgen Schmidhuber · 2002
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Apprenticeship Learning via Inverse Reinforcement Learning
Pieter Abbeel and Andrew Y Ng · 2004
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Arbitrarily coherent preferences
Dan Ariely, George Loewenstein, and Drazen Prelec · 2004
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Maximum margin planning
Nathan D Ratliff, J Andrew Bagnell, and Martin A Zinkevich · 2006
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Maximum Entropy Inverse Reinforcement Learning
Brian D Ziebart, Andrew Maas, J Andrew Bagnell, and Anind K Dey · 2008
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Neuroeconomics: Decision making and the brain, 2009
Paul W Glimcher, Colin F Camerer, Ernst Fehr, and Russell A Poldrack · 2009
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Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange
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Research Priorities for Robust and Beneficial Artificial Intelligence
Stuart Russell, Daniel Dewey, and Max Tegmark · 2015
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Bias blind spot: Structure, measurement, and consequences
Irene Scopelliti, Carey K Morewedge, Erin McCormick, H Lauren Min, Sophie Lebrecht, and Karim S Kassam · 2015
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Towards Resolving Unidentifiability in Inverse Reinforcement Learning
Kareem Amin and Satinder Singh · 2016
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Concrete Problems in AI Safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Avoiding wireheading with value reinforcement learning
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Thinking, fast and slow , volume 1
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