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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 …
states—can be formalized as inverse reinforcement learning. Under this framework …
An overview of machine teaching
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 …
presented as varying along a dimension. The collection of dimensions then form the …
In situ bidirectional human-robot value alignment
A prerequisite for social coordination is bidirectional communication between teammates,
each playing two roles simultaneously: as receptive listeners and expressive speakers. For …
each playing two roles simultaneously: as receptive listeners and expressive speakers. For …
Learning to teach
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 …
educating our next generations. A good teacher will select appropriate teaching materials …
Learning to teach with dynamic loss functions
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 …
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
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 …
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
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 …
model. Explainable AI (XAI) attempts to improve human understanding but rarely accounts …
Leveraging human guidance for deep reinforcement learning tasks
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 …
with the environment. Human knowledge of how to solve these tasks can be incorporated …
Human-in-the-loop imitation learning using remote teleoperation
Imitation Learning is a promising paradigm for learning complex robot manipulation skills by
reproducing behavior from human demonstrations. However, manipulation tasks often …
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
Those designing autonomous systems that interact with humans will invariably face
questions about how humans think and make decisions. Fortunately, computational …
questions about how humans think and make decisions. Fortunately, computational …