# A Survey on Multi-User Conversational Interfaces
## 4 Capturing Multi-User Conversational Interfaces
### Application Domains and Examples
#### 4.1.5 Negotiation and Conflict Resolution  
A variety of multi-party dialogue scenarios have been explored in the context of negotiation dialogues and conflict resolution. For negotiation dialogues, game-like structures such as 'Settlers of Catan' [29; 30; 31] have been used where multiple agents are expected to negotiate trades to be successful in the game. However, these scenarios are designed as purely simulated environments, with no direct involvement of human participants, to study uncooperative interaction strategies.  
In a separate theoretical framework, a dialogue game has been conceptualised, involving multiple artificial agents engaged in resolving belief conflicts through argumentation [32]. Notably, this framework does not involve human participants, focusing exclusively on artificial agents. It primarily serves as a theoretical construct without a corresponding implementation.  
[PERSON] et al. [33] discuss the development of a virtual assistant for conflict resolution within a smart home environment. The MU system adapts its operations to individual routines and preferences of its users, creating profiles to personalise interactions and recommendations. It continuously monitors user activities and scheduled events, offering timely prompts and reminders to maintain routines and safety. The system is also designed to detect and respond to potential risks in the home environment, issuing alerts to prevent harm. Overall, the proposed system integrates personalised assistance with proactive safety management to enhance the user experience.  
The analysis of application domains also reveals that the design and implementation of MUCIs are influenced by various factors, with two key dimensions standing out:  
* MU collaborative interactions vs. MU competitive interactions: For example, in social conversation contexts, tasks are often collaborative in nature [11; 13], with usersworking together toward a shared goal. In contrast, competitive scenarios consist of a different dynamic, where users may interact with the system in parallel, without being explicitly aware of each other's intentions and ambitions. This can lead to implicit or explicit competition, where users compete for the system's attention or resources, either unintentionally, as observed in [10], or deliberately, as demonstrated in [21; 22]. Such interactions between users influence both the task and behaviour of the agent and thus require a different optimisation of the dialogue policy.
* Task-oriented designs vs. open-domain designs: Another meaningful way to categorise the reviewed works is by distinguishing between task-oriented and open-domain systems. Task-oriented systems are specifically designed and customised to achieve a specific objective, such as Tangy [12], which assists users in scheduling and playing bingo games. In contrast, open-domain systems aim to engage in open conversations without a narrowly defined goal, as shown in [15; 16]. These differences in system goals also have significant implications for dialogue policy design and the handling of MU dialogues. While user interactions are often structured and goal-driven in task-oriented dialogues, reducing the need for extensive individual customisation, open-domain systems may require more nuanced adaptation to individual users and in-group dynamics to maintain engaging and contextually appropriate interactions. Moreover, the application of user models may facilitate the decision-making in domains such as tutoring [25; 26] or conflict resolution [33].  
As we can observe, the user and dialogue modelling plays a key role in the design of MUCIs. Consequently, the following section offers a more in-depth analysis of this topic.