32 citations · 79 across the 8 of their papers we have counts for
10 papers · 1 filter
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…
Contrastive Explanations of Plans Through Model Restrictions
Benjamin Krarup, Senka Krivic, Daniele Magazzeni +3
In automated planning, the need for explanations arises when there is a mismatch between a proposed plan and the user's expectation. We frame Explainable AI Planning in the context…
Model Elicitation through Direct Questioning
Sachin Grover, David Smith, Subbarao Kambhampati
The future will be replete with scenarios where humans are robots will be working together in complex environments. Teammates interact, and the robot's interaction has to be about…
A Bayesian Account of Measures of Interpretability in Human-AI Interaction
Sarath Sreedharan, Anagha Kulkarni, Tathagata Chakraborti +2
Existing approaches for the design of interpretable agent behavior consider different measures of interpretability in isolation. In this paper we posit that, in the design and depl…
FAPE: a Constraint-based Planner for Generative and Hierarchical Temporal Planning
Arthur Bit-Monnot, Malik Ghallab, Félix Ingrand +1
Temporal planning offers numerous advantages when based on an expressive representation. Timelines have been known to provide the required expressiveness but at the cost of search…
Why Couldn't You do that? Explaining Unsolvability of Classical Planning Problems in the Presence of Plan Advice
Sarath Sreedharan, Siddharth Srivastava, David Smith +1
Explainable planning is widely accepted as a prerequisite for autonomous agents to successfully work with humans. While there has been a lot of research on generating explanations…