activity
20172022
most citedNatural Language Interaction with Explainable AI Models

7 citations · 21 across the 9 of their papers we have counts for

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Showing cs.AIShow all

6 papers · 1 filter

cs.AI2022

DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agents

Ziqiao Ma, Ben VanDerPloeg, Cristian-Paul Bara +5

In the real world, autonomous driving agents navigate in highly dynamic environments full of unexpected situations where pre-trained models are unreliable. In these situations, wha…

cs.AI2022

DANLI: Deliberative Agent for Following Natural Language Instructions

Yichi Zhang, Jianing Yang, Jiayi Pan +6

Recent years have seen an increasing amount of work on embodied AI agents that can perform tasks by following human language instructions. However, most of these agents are reactiv…

cs.AI20216 cited

Hierarchical Task Learning from Language Instructions with Unified Transformers and Self-Monitoring

Yichi Zhang, Joyce Chai

Despite recent progress, learning new tasks through language instructions remains an extremely challenging problem. On the ALFRED benchmark for task learning, the published state-o…

cs.AI2019

X-ToM: Explaining with Theory-of-Mind for Gaining Justified Human Trust

Arjun R. Akula, Changsong Liu, Sari Saba-Sadiya +4

We present a new explainable AI (XAI) framework aimed at increasing justified human trust and reliance in the AI machine through explanations. We pose explanation as an iterative c…

cs.AI20197 cited

Natural Language Interaction with Explainable AI Models

Arjun R Akula, Sinisa Todorovic, Joyce Y Chai +1

This paper presents an explainable AI (XAI) system that provides explanations for its predictions. The system consists of two key components -- namely, the prediction And-Or graph…

cs.AI2017

Interactive Learning of State Representation through Natural Language Instruction and Explanation

Qiaozi Gao, Lanbo She, Joyce Y. Chai

One significant simplification in most previous work on robot learning is the closed-world assumption where the robot is assumed to know ahead of time a complete set of predicates…