# A Survey on Multi-User Conversational Interfaces
## 4 Capturing Multi-User Conversational Interfaces
### User and Dialogue Modelling
[43] implement the Platon system, which reduces the complexity of modelling by creating a new dialogue engine instance for each user. These instances interact via shared variables or message exchanges, functioning similarly to text input handling. Platon employs a hierarchical task decomposition approach to manage multi-user scenarios by structuring sub-tasks as agents with defined input-handling rules. Originally developed for an interactive multi-user game, the system ensures modular and scalable dialogue management. A key aspect of MUCI is turn-taking, which [PERSON] and [PERSON] [3] address by proposing a model for deciding when and what to say in a conversation. Moreover, [PERSON] et al. [22] investigate a bartender robot designed to handle multiple users by tracking social states, including customer attention-seeking behaviour and order fulfilment status. The system processes continuous input from low-level vision and speech components, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Publication**} & \multirow{2}{*}{**User Model**} & **Decision Making for Turn-Taking** &
\begin{tabular}{c} **Implemented** \\ **System Prototype** \\ \end{tabular} & \multicolumn{2}{c}{**Considering**} \\  & & **for** **Turn-Taking** & **System Prototype** & **In-Group Dynamics** \\ \hline
[21; 35; 36] & ✓ & ✓ & ✓ & ✓ \\ \hline
[3] & ✓ & ✓ & ✓ & \\ \hline
[25] & ✓ & ✓ & & ✓ \\ \hline
[14] & ✓ & ✓ & & \\ \hline
[11; 12; 33; 37; 38; 39] & ✓ & & ✓ & ✓ \\ \hline
[40; 41] & ✓ & & & ✓ \\ \hline
[42] & ✓ & & & \\ \hline
[22; 28] & ✓ & ✓ & ✓ & ✓ \\ \hline
[15; 16; 34] & ✓ & ✓ & & \\ \hline
[43; 44] & ✓ & & & \\ \hline
[45] & & & ✓ & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristics of publications with regard to their user and dialogue modelling approaches. fusing these data sources to assign speech hypotheses and estimate customer engagement. The model captures state hypotheses with confidence scores and considers alternative interpretations of drink orders. Participation of conversational interfaces in group interactions is investigated by [PERSON] et al. [36], where the authors describe a chatbot that engages in discussions by moderating rather than providing expert knowledge. Meanwhile, [PERSON] et al. [25] focus on the collection of a human-human multi-modal corpus featuring three participants engaged in a reading comprehension task within an educational setup. The study analyses multi-modal interaction strategies, communicative functions, and behavioural patterns, contributing insights into the design of conversational systems by identifying key conversational features such as dominance, attention, and engagement. The work of [PERSON] et al. [41] presents a deep learning-based approach to person-specific response generation in multi-party conversations. Using the Multi-WoO dataset, the model constructs persona embeddings that capture individual conversational behaviours. The system encodes utterances using gated recurrent units and is tested with transformer models, seq2 seq, and generative adversarial networks. A dyadic speaker-addressee model is employed to capture interaction dynamics, where utterances are classified as central or context, ensuring that responses align with a speaker's conversational style. In the paper of [PERSON] et al. [34], it is examined how a robot-assisted bingo game operates in a residential care setting, utilising user state modelling to enhance engagement. The Assistance Behavior Deliberation module determines the robot [PERSON]'s actions using a finite state machine, ensuring appropriate responses to player requests. When multiple assistance requests are made, they are queued in the order buttons are pressed, as users cannot interact via speech and only receive instructions from the robot. Similarly, [PERSON] et al. [12] investigate user state identification in household conflicts, where a virtual assistant provides both group-based and individualised assistance based on scheduled activities.