Ontological foundations for contrastive explanatory narration of robot plans
This work addresses the need for trustworthy human-robot interaction by improving how robots communicate their decisions, though it appears incremental as it builds on existing ontology-based methods.
The paper tackles the problem of enabling robots to explain their decisions by comparing competing plans, proposing an ontological model and a novel algorithm for contrastive explanatory narration, with empirical evaluation showing it outperforms a baseline method.
Mutual understanding of artificial agents' decisions is key to ensuring a trustworthy and successful human-robot interaction. Hence, robots are expected to make reasonable decisions and communicate them to humans when needed. In this article, the focus is on an approach to modeling and reasoning about the comparison of two competing plans, so that robots can later explain the divergent result. First, a novel ontological model is proposed to formalize and reason about the differences between competing plans, enabling the classification of the most appropriate one (e.g., the shortest, the safest, the closest to human preferences, etc.). This work also investigates the limitations of a baseline algorithm for ontology-based explanatory narration. To address these limitations, a novel algorithm is presented, leveraging divergent knowledge between plans and facilitating the construction of contrastive narratives. Through empirical evaluation, it is observed that the explanations excel beyond the baseline method.