activity
20192023
most citedOn the Utility of Learning about Humans for Human-AI Coordination

91 citations · 121 across the 12 of their papers we have counts for

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Showing cs.LGShow all

5 papers · 1 filter

cs.LG2023

Structurally guided task decomposition in spatial navigation tasks

Ruiqi He, Carlos G. Correa, Thomas L. Griffiths +1

How are people able to plan so efficiently despite limited cognitive resources? We aimed to answer this question by extending an existing model of human task decomposition that can…

cs.LG20232 cited

Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation

Andi Peng, Aviv Netanyahu, Mark Ho +4

Policies often fail due to distribution shift -- changes in the state and reward that occur when a policy is deployed in new environments. Data augmentation can increase robustness…

cs.LG20231 cited

Bayesian Reinforcement Learning with Limited Cognitive Load

Dilip Arumugam, Mark K. Ho, Noah D. Goodman +1

All biological and artificial agents must learn and make decisions given limits on their ability to process information. As such, a general theory of adaptive behavior should be ab…

cs.LG2022

On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning

Dilip Arumugam, Mark K. Ho, Noah D. Goodman +1

Throughout the cognitive-science literature, there is widespread agreement that decision-making agents operating in the real world do so under limited information-processing capabi…

cs.LG201991 cited

On the Utility of Learning about Humans for Human-AI Coordination

Micah Carroll, Rohin Shah, Mark K. Ho +4

While we would like agents that can coordinate with humans, current algorithms such as self-play and population-based training create agents that can coordinate with themselves. Ag…