  With the growing capabilities of intelligent systems, the integration of
artificial intelligence (AI) and robots in everyday life is increasing.
However, when interacting in such complex human environments, the failure of
intelligent systems, such as robots, can be inevitable, requiring recovery
assistance from users. In this work, we develop automated, natural language
explanations for failures encountered during an AI agents' plan execution.
These explanations are developed with a focus of helping non-expert users
understand different point of failures to better provide recovery assistance.
Specifically, we introduce a context-based information type for explanations
that can both help non-expert users understand the underlying cause of a system
failure, and select proper failure recoveries. Additionally, we extend an
existing sequence-to-sequence methodology to automatically generate our
context-based explanations. By doing so, we are able develop a model that can
generalize context-based explanations over both different failure types and
failure scenarios.

Artificial Intelligence;Robotics