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

arXiv: https://arxiv.org/abs/2108.02174 · HTML: https://mlsanity.com/paper/2108.02174/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2021.

## Built on by (most cited first, 7 of 7)

- [How Can Large Language Models Enable Better Socially Assistive Human-Robot Interaction: A Brief Survey](https://mlsanity.com/paper/2404.00938/) (2024)
- [We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses](https://mlsanity.com/paper/2310.06245/) (2023)
- [Enhancing User-Oriented Proactivity in Open-Domain Dialogues with Critic Guidance](https://mlsanity.com/paper/2505.12334/) (2025)
- [MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning](https://mlsanity.com/paper/2306.02252/) (2023)
- [Cognitively Inspired Components for Social Conversational Agents](https://mlsanity.com/paper/2311.05450/) (2023)
- [Unsupervised Flow Discovery from Task-oriented Dialogues](https://mlsanity.com/paper/2405.01403/) (2024)
- [Moderating Group Conversation Dynamics with Social Robots](https://mlsanity.com/paper/2408.00151/) (2024)

## Abstract (arXiv)

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's input. Moreover, it discusses strategies to ensure a conversation flow that captures, as more coherently as possible, the user's 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.