Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters

2026-04-20 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, long

Summary

A new paper by Sun et al. proposes a consistent conceptual foundation for understanding uncertainty, vagueness, and ambiguity (UVA-phenomena) in human-robot interaction (HRI). The authors note that these concepts are often confused and inconsistently defined in existing literature, hindering empirical comparability and theoretical development. They establish clear definitions for each term, grounded in dictionary meanings, and analyze their distinctions and interrelationships within HRI. Uncertainty relates to incomplete knowledge, vagueness to indeterminate boundaries, and ambiguity to multiple interpretations of expressions. The paper categorizes these phenomena further into types like epistemic/aleatoric uncertainty, epistemic/semantic vagueness, and lexical/syntactic/pragmatic/semantic ambiguity. This framework is then used to illustrate how a precise conceptualization facilitates the design of novel HRI methods and the evaluation of existing methodologies, such as the KnowNo, HYNA, and OSSA frameworks.

Key takeaway

For AI Scientists and Robotics Engineers developing HRI systems, understanding the precise distinctions between uncertainty, vagueness, and ambiguity is critical. This conceptual clarity enables you to design more robust models that specifically address the root causes of communication and interaction failures. Your models should explicitly account for factors like incomplete knowledge, imprecise concepts, and multiple interpretations to build more trustworthy and reliable AI agents.

Key insights

Consistent conceptualization of uncertainty, vagueness, and ambiguity is crucial for advancing human-robot interaction.

Principles

• UVA-phenomena are distinct but overlapping.
• Root causes of UVA-phenomena differ.
• Precise conceptualization guides model design.

Method

The proposed method involves defining UVA-phenomena based on dictionary meanings, interpreting them in HRI context, categorizing their types (e.g., epistemic, aleatoric), and illustrating their application through examples to guide model development and evaluation.

In practice

• Categorize uncertainty into epistemic and aleatoric.
• Distinguish vagueness as epistemic or semantic.
• Classify ambiguity by lexical, syntactic, pragmatic, semantic levels.

Topics

• Human-Robot Interaction
• Conceptual Foundation
• Uncertainty
• Vagueness
• Ambiguity

Best for: AI Scientist, Robotics Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.