Publication
Ontological foundations for contrastive explanatory narration of robot plans
Journal Article (2026)
Journal
Information Sciences
Pages
741
Volume
123280
Doc link
https://doi.org/10.1016/j.ins.2026.123280
File
Authors
Projects associated
Abstract
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.
Categories
artificial intelligence, intelligent robots, knowledge engineering, planning (artificial intelligence).
Author keywords
Applied ontology, Reasoning for robots, Robotics, Contrastive explainable robots, Explanatory narratives construction
Scientific reference
A. Olivares-Alarcos, S. Foix, J. Borràs, G. Canal and G. Alenyà. Ontological foundations for contrastive explanatory narration of robot plans. Information Sciences, 123280: 741, 2026.
Follow us!