Knowledge-Grounded Dialogue Flow Management for Social Robots and Conversational Agents

Abstract

The article proposes a system for knowledge-based conversation designed for Social Robots and other conversational agents. The proposed system relies on an Ontology for the description of all concepts that may be relevant conversation topics, as well as their mutual relationships. The article focuses on the algorithm for Dialogue Management that selects the most appropriate conversation topic depending on the user input. Moreover, it discusses strategies to ensure a conversation flow that captures, as more coherently as possible, the user intention to drive the conversation in specific directions while avoiding purely reactive responses to what the user says. To measure the quality of the conversation, the article reports the tests performed with 100 recruited participants, comparing five conversational agents: (i) an agent addressing dialogue flow management based only on the detection of keywords in the speech, (ii) an agent based both on the detection of keywords and the Content Classification feature of Google Cloud Natural Language, (iii) an agent that picks conversation topics randomly, (iv) a human pretending to be a chatbot, and (v) one of the most famous chatbots worldwide: Replika. The subjective perception of the participants is measured both with the SASSI (Subjective Assessment of Speech System Interfaces) tool, as well as with a custom survey for measuring the subjective perception of coherence.

Sustainable Verbal and Non-verbal Human-Robot Interaction Through Cloud Services

“…The system described in this paper is currently being used in our laboratory for research in Human-Robot Interaction (Figure 7). Experiments to evaluate the system's capabilities in terms of the quality of the interaction, usability, as well as the impact on life quality were performed with care home residents in the UK and Japan [7]. The goal of the experiments described in this article is to analyze the system performance in terms of response speed when multiple clients connect to the cloud in different configurations.…”

“…Finally, it is worth anticipating that the ability of the system to understand the user's intention of making the device perform an action (Section III-C) and naturally converse with the user with mixed-initiative dialogues (Section III-D) is based on a framework for cultural knowledge representation that relies on an OWL 2 Ontology [4]. Being paralleled by a framework for probabilistic reasoning, such an Ontology is designed to take into account the possible cultural differences between different users in a non-stereotyped way, both concerning conversation and action [5,6,7]. A mechanism to expand in run-time the cultural knowledge base with new concepts and conversation topics raised by the users (possibly selfidentifying with different cultures) was introduced in [8].…”

A social robot connected with chatGPT to improve cognitive functioning in ASD subjects

“…The adaptive robotic system using Nao has demonstrated promising results in improving social interaction and communication skills in young children with autism (Bekele et al, 2014 ). Systematic reviews have highlighted the positive effects of robot-enhanced therapy, including improvements in social, cognitive, and emotional skills in children with ASD (Peca et al, 2016 ; David et al, 2020 ; van Otterdijk et al, 2020 ; Chung, 2021 ; Ghiglino et al, 2021 ; Grassi et al, 2022 ).…”

A Survey on Recent Advances in Social Robotics

“…It covered different aspects such as therapy bots, avatars, and intelligent animal-like robots. Additionally, an algorithm for dialogue management has been proposed in [106] for social robots and conversational agents. It is aimed at ensuring a rich and interesting conversation with users.…”