153 citations · 716 across the 39 of their papers we have counts for
9 papers · 1 filter
Explaining Reward Functions to Humans for Better Human-Robot Collaboration
Lindsay Sanneman, Julie Shah
Explainable AI techniques that describe agent reward functions can enhance human-robot collaboration in a variety of settings. One context where human understanding of agent reward…
Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty
Shen Li, Theodoros Stouraitis, Michael Gienger +2
Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models. In this work, we propose a…
The Irrationality of Neural Rationale Models
Yiming Zheng, Serena Booth, Julie Shah +1
Neural rationale models are popular for interpretable predictions of NLP tasks. In these, a selector extracts segments of the input text, called rationales, and passes these segmen…
Emergent Discrete Communication in Semantic Spaces
Mycal Tucker, Huao Li, Siddharth Agrawal +4
Neural agents trained in reinforcement learning settings can learn to communicate among themselves via discrete tokens, accomplishing as a team what agents would be unable to do al…
Supervised Bayesian Specification Inference from Demonstrations
Ankit Shah, Pritish Kamath, Shen Li +4
When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that ta…
Do Feature Attribution Methods Correctly Attribute Features?
Yilun Zhou, Serena Booth, Marco Tulio Ribeiro +1
Feature attribution methods are popular in interpretable machine learning. These methods compute the attribution of each input feature to represent its importance, but there is no…
A Bayesian Approach to Identifying Representational Errors
Ramya Ramakrishnan, Vaibhav Unhelkar, Ece Kamar +1
Trained AI systems and expert decision makers can make errors that are often difficult to identify and understand. Determining the root cause for these errors can improve future de…
Reactive Task and Motion Planning under Temporal Logic Specifications
Shen Li, Daehyung Park, Yoonchang Sung +2
We present a task-and-motion planning (TAMP) algorithm robust against a human operator's cooperative or adversarial interventions. Interventions often invalidate the current plan a…
Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare
Sangwon Seo, Lauren R. Kennedy-Metz, Marco A. Zenati +3
Shared mental models are critical to team success; however, in practice, team members may have misaligned models due to a variety of factors. In safety-critical domains (e.g., avia…