Senka Krivić

2 accepted papers

2022

PlanVerb: Domain-Independent Verbalization and Summary of Task Plans

Gerard Canal, Senka Krivić, Paul Luff, Andrew Coles

AAAI 2022technical

For users to trust planning algorithms, they must be able to understand the planner's outputs and the reasons for each action selection. This output does not tend to be user-friendly, often consisting of sequences of parametrised actions or task networks. And these may not be practical for non-exper…

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2021

Towards providing explanations for robot motion planning

Martim Brandão, Gerard Canal, Senka Krivić, Daniele Magazzeni

ICRA 2021poster

Recent research in AI ethics has put forth explainability as an essential principle for AI algorithms. However, it is still unclear how this is to be implemented in practice for specific classes of algorithms—such as motion planners. In this paper we unpack the concept of explanation in the context…

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