Knowledge graphs are often used to represent structured information in a flexible and efficient manner, but their use in situated dialogue remains under-explored. This paper presents a novel conversational model for human–robot interaction that rests upon a graph-based representation of the dialogue state. The knowledge graph representing the dialogue state is continuously updated with new observations from the robot sensors, including linguistic, situated and multimodal inputs, and is further enriched by other modules, in particular for spatial understanding. The neural conversational model employed to respond to user utterances relies on a simple but effective graph-to-text mechanism that traverses the dialogue state graph and converts the traversals into a natural language form. This conversion of the state graph into text is performed using a set of parameterized functions, and the values for those parameters are optimized based on a small set of Wizard-of-Oz interactions. After this conversion, the text representation of the dialogue state graph is included as part of the prompt of a large language model used to decode the agent response. The proposed approach is empirically evaluated through a user study with a humanoid robot that acts as conversation partner to evaluate the impact of the graph-to-text mechanism on the response generation. After moving a robot along a tour of an indoor environment, participants interacted with the robot using spoken dialogue and evaluated how well the robot was able to answer questions about what the robot observed during the tour. User scores suggest an improvement in the perceived factuality of the robot responses when the graph-to-text approach is employed compared to a baseline using inputs structured as semantic triples.

I am interested graph-based dialogue management for dialogue systems, specifically the use of knowledge- graphs. Representations of knowledge combining in- formation about the world with dialogue or user-specific information, such as personal knowledge graphs (Balog and Kenter, 2019) are of particular interest to me. Knowl- edge graphs have the flexibility to represent diverse in- formation such as dialogue specific information, gen- eral world knowledge, and even situated knowledge in the case of embodied dialogue systems. Much of my previous work has investigated knowledge graphs in an HRI context that combined these attributes (Walker et al., 2022b).

This paper explores the integration of voice-controlled dialogue systems in narrative-driven video games, addressing the limitations of existing approaches. We propose a hybrid interface that allows players to freely paraphrase predefined dialogue options, combining player expressiveness with narrative cohesion. The prototype was developed in Unity, and a large language model was used to map the transcribed voice input to existing dialogue options. The approach was evaluated in a user study (n=14) that compared the hybrid interface to traditional point-and-click methods. Results indicate the proposed interface enhances player’s degree of joy and perceived freedom while maintaining narrative consistency. The findings provide insights into the design of scalable and engaging voice-controlled systems for interactive storytelling. Future research should focus on reducing latency and refining language model accuracy to further improve user experience and immersion.

In this paper, we present an approach for extracting knowledge graph information for retrieval augmented generation in dialogue systems. Knowledge graphs are a rich source of background information, but the inclusion of more potentially useful information in a system prompt risks decreased model performance from excess context. We investigate a method of retrieving relevant subgraphs of maximum relevance and minimum size by framing this trade-off as a Prize-collecting Steiner Tree problem. The results of our user study and analysis indicate promising efficacy of a simple subgraph retrieval approach compared with a top-K retrieval model.

I am a postdoctoral researcher at Otto-Friedrich University of Bamberg, and my research interests include the knowledge-grounded dialogue systems, logical rule-based reasoning for dialogue management, and human-robot interaction.