Ontological foundations for contrastive explanatory narration of robot plans

2025-09-26

AI Summary

This study addresses the lack of interpretability in robotic decision-making, which undermines human-robot trust. We propose an ontology-based comparative explanation generation framework. Methodologically, we construct a domain ontology modeling competitive multi-plan relationships and design a difference-driven inference algorithm that automatically generates contrastive narratives highlighting critical distinctions among alternatives. Our key contributions are: (1) the first integration of ontological modeling with contrastive storytelling, enabling a paradigm shift from “single-plan explanations” to “multi-plan differential explanations”; and (2) an algorithm that overcomes baseline limitations in representing differential knowledge and structuring explanatory narratives. Experiments demonstrate statistically significant improvements over state-of-the-art methods in explanation clarity, effectiveness of difference communication, and human comprehension—establishing a verifiable foundation for trustworthy human-robot collaboration.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Modeling differences between competing robot plans
• Developing algorithms for contrastive explanatory narration
• Enhancing robot decision communication to humans

Innovation

• Novel ontological model formalizes differences between competing plans
• Algorithm leverages divergent knowledge for contrastive narratives
• Classification of optimal plans based on predefined criteria