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20162026
most citedOn the Planning Abilities of Large Language Models : A Critical Investigation

53 citations · 284 across the 32 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

cs.AI2021

Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems

Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma +2

Despite the surprising power of many modern AI systems that often learn their own representations, there is significant discontent about their inscrutability and the attendant prob…

cs.AI2021

Not all users are the same: Providing personalized explanations for sequential decision making problems

Utkarsh Soni, Sarath Sreedharan, Subbarao Kambhampati

There is a growing interest in designing autonomous agents that can work alongside humans. Such agents will undoubtedly be expected to explain their behavior and decisions. While g…

cs.CL2021★ 27 cited

GPT3-to-plan: Extracting plans from text using GPT-3

Alberto Olmo, Sarath Sreedharan, Subbarao Kambhampati

Operations in many essential industries including finance and banking are often characterized by the need to perform repetitive sequential tasks. Despite their criticality to the b…

cs.AI2021

Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction

Zahra Zahedi, Mudit Verma, Sarath Sreedharan +1

Trust between team members is an essential requirement for any successful cooperation. Thus, engendering and maintaining the fellow team members' trust becomes a central responsibi…

cs.AI2021

A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI

Sarath Sreedharan, Anagha Kulkarni, David E. Smith +1

Existing approaches for generating human-aware agent behaviors have considered different measures of interpretability in isolation. Further, these measures have been studied under…