Plan Verbalisation for Robots Acting in Dynamic Environments

Automated planning provides the tools for intelligent behaviours in robotic platforms deployed in real-world environments. The complexity of these domains requires planning models that support the system’s dynamics. This results in AI planning approaches often generating plans where the reason- ing around the solution remains obscure for the operator/user. This lack of transparency can reduce trust, results in frequent interventions, and ultimately represents a barrier to adopting autonomous systems. Explanations of behaviour in an easy- to-understand manner, such as in natural language, can help the user comprehend the reasoning behind autonomous actions and help build an accurate mental model. This paper presents an approach for a type of explanation, namely plan verbalisation, that considers the properties of the planning model and describes the system behaviour during plan execution, including replanning and plan repair. We use natural language techniques to support the disambiguation of the robot decision-making process, considering the planning model encapsulated using the Planning Domain Definition Language (PDDL). The system is evaluated using an Autonomous Underwater Vehicle (AUV) inspection use case.