Theory of mind as inverse reinforcement learning

J Jara-Ettinger - Current Opinion in Behavioral Sciences, 2019 - Elsevier
We review the idea that Theory of Mind—our ability to reason about other people's mental
states—can be formalized as inverse reinforcement learning. Under this framework …

An overview of machine teaching

X Zhu, A Singla, S Zilles, AN Rafferty - arxiv preprint arxiv:1801.05927, 2018 - arxiv.org
In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is
presented as varying along a dimension. The collection of dimensions then form the …

In situ bidirectional human-robot value alignment

L Yuan, X Gao, Z Zheng, M Edmonds, YN Wu… - Science robotics, 2022 - science.org
A prerequisite for social coordination is bidirectional communication between teammates,
each playing two roles simultaneously: as receptive listeners and expressive speakers. For …

Learning to teach

Y Fan, F Tian, T Qin, XY Li, TY Liu - arxiv preprint arxiv:1805.03643, 2018 - arxiv.org
Teaching plays a very important role in our society, by spreading human knowledge and
educating our next generations. A good teacher will select appropriate teaching materials …

Learning to teach with dynamic loss functions

L Wu, F Tian, Y **a, Y Fan, T Qin… - Advances in neural …, 2018 - proceedings.neurips.cc
Teaching is critical to human society: it is with teaching that prospective students are
educated and human civilization can be inherited and advanced. A good teacher not only …

[HTML][HTML] Theory of mind and preference learning at the interface of cognitive science, neuroscience, and AI: A review

C Langley, BI Cirstea, F Cuzzolin… - Frontiers in artificial …, 2022 - frontiersin.org
Theory of Mind (ToM)-the ability of the human mind to attribute mental states to others-is a
key component of human cognition. In order to understand other people's mental states or …

Mitigating belief projection in explainable artificial intelligence via Bayesian teaching

SCH Yang, WK Vong, RB Sojitra, T Folke, P Shafto - Scientific reports, 2021 - nature.com
State-of-the-art deep-learning systems use decision rules that are challenging for humans to
model. Explainable AI (XAI) attempts to improve human understanding but rarely accounts …

Leveraging human guidance for deep reinforcement learning tasks

R Zhang, F Torabi, L Guan, DH Ballard… - arxiv preprint arxiv …, 2019 - arxiv.org
Reinforcement learning agents can learn to solve sequential decision tasks by interacting
with the environment. Human knowledge of how to solve these tasks can be incorporated …

Human-in-the-loop imitation learning using remote teleoperation

A Mandlekar, D Xu, R Martín-Martín, Y Zhu… - arxiv preprint arxiv …, 2020 - arxiv.org
Imitation Learning is a promising paradigm for learning complex robot manipulation skills by
reproducing behavior from human demonstrations. However, manipulation tasks often …

Cognitive science as a source of forward and inverse models of human decisions for robotics and control

MK Ho, TL Griffiths - Annual Review of Control, Robotics, and …, 2022 - annualreviews.org
Those designing autonomous systems that interact with humans will invariably face
questions about how humans think and make decisions. Fortunately, computational …