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An Ontology-Driven System for Diversity-Aware Human-Robot Interaction Using LLMs

Year: 2024

Type: conference-paper

Abstract: This extended abstract presents a novel system for diversity-aware autonomous conversations using Large Language Models (LLMs). Leveraging a cloud-based architecture and an ontology-driven knowledge base, the system adapts interactions to a wide range of user characteristics. Experiments in both controlled settings and real-world case studies demonstrate the system's effectiveness in managing complex interactions and sustaining user engagement across various environments.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14731010

Authors: Grassi, Lucrezia, Recchiuto, Carmine, Sgorbissa, Antonio

Language: English

Topic: Speech and dialogue systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6949659969

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