Single or Multiple Conversational Agents?

Chaves, Ana Paula; Gerosa, Marco Aurélio

doi:10.1145/3173574.3173765

Cited by 40 publications

(9 citation statements)

References 39 publications

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“…On the other hand, some players preferred the simplicity of contact to a single agent and perceived to succeed faster in the game this way, even though the duration of the responses were the same in both versions. They experienced the multi-agent interaction as too complex and overstraining, which is in line with results from a study on a multi-chatbot system [7]. This seems conclusive considering the brevity of the exposure to the multi-agent system with roughly 10 minutes per session.…”

Section: Discussionsupporting

confidence: 86%

“…They encountered reluctance from the users when the agents conversed with each other as this was considered unnecessary in a task-oriented setting [17]. In another study on the user's behavior towards multiagent systems, Chaves and Gerosa [7] analyzed the change in speech and reactions of users to a multiplicity of chatbots compared to a single chatbot. Their results showed that the multiplicity of chatbots had no significant effect on the conversation structure or its content.…”

Section: Multi-agent Interactionmentioning

confidence: 99%

See 1 more Smart Citation

Smells Like Team Spirit

Zargham

Bonfert

Volkmar

et al. 2020

Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play

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Verbal communication is a central component in collaborative multiplayer gaming and creates a feeling of companionship among the players. In single-player games, this aspect is often missing. Advancements in speech recognition now open new potentials for voice-activated single-player experiences. In this work, we integrated voice interaction to a single-player virtual reality (VR) game. To create a sense of team spirit, we enabled players to talk to a multiplicity of agents using natural language. We hypothesize that conversing with only one agent cannot produce the same level of camaraderie. We conducted a preliminary qualitative user study (=10) to explore how players experience talking with the in-game characters in the single-agent and the multi-agent condition. Early results suggest that our participants prefer interacting with the group of interlocutors. They perceived the multi-agent condition as more entertaining and liked the feeling of being part of a team. CCS CONCEPTS • Human-centered computing → Natural language interfaces; Virtual reality; • Applied computing → Computer games.

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Section: Discussionsupporting

confidence: 86%

Section: Multi-agent Interactionmentioning

confidence: 99%

Smells Like Team Spirit

Zargham

Bonfert

Volkmar

et al. 2020

Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play

View full text Add to dashboard Cite

show abstract

“…The Priority maxim suggests that power dynamics are at play in human-AI interactions at a turn-taking level, where humans are assumed to take priority in initiating turns. Developing proper turn-taking strategies is notoriously difficult in AI design (Portela & Granell-Canut 2017;Liao et al 2016;Chaves & Gerosa 2018). Taking the Priority maxim into account could help resolve some of these issues, where designers can take human expectations of their priority into account.…”

Section: Resultsmentioning

confidence: 99%

Human-AI interactions through a Gricean lens

Panfili

Duman

Nave³

et al. 2021

Proc Ling Soc Amer

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Grice’s Cooperative Principle (1975), which describes the implicit maxims that guide effective conversation, has long been applied to conversations between humans. However, as humans begin to interact with non-human dialogue systems more frequently and in a broader scope, an important question emerges: what principles govern those interactions? In the present study, this question is addressed, as human-AI interactions are categorized using Grice’s four maxims. In doing so, it demonstrates the advantages and shortcomings of such an approach, ultimately demonstrating that humans do, indeed, apply these maxims to interactions with AI, even making explicit references to the AI’s performance through a Gricean lens. Twenty-three participants interacted with an American English-speaking Alexa and rated and discussed their experience with an in-lab researcher. Researchers then reviewed each exchange, identifying those that might relate to Grice’s maxims: Quantity, Quality, Manner, and Relevance. Many instances of explicit user frustration stemmed from violations of Grice’s maxims. Quantity violations were noted for too little but not too much information, while Quality violations were rare, indicating high trust in Alexa’s responses. Manner violations focused on speed and humanness. Relevance violations were the most frequent of all violations, and they appear to be the most frustrating. While the maxims help describe many of the issues participants encountered with Alexa’s responses, other issues do not fit neatly into Grice’s framework. For example, participants were particularly averse to Alexa initiating exchanges or making unsolicited suggestions. To address this gap, we propose the addition of human Priority to describe human-AI interaction. Humans and AIs are not (yet?) conversational equals, and human initiative takes priority. Moreover, we find that Relevance is of particular importance in human-AI interactions and suggest that the application of Grice’s Cooperative Principles to human-AI interactions is beneficial both from an AI development perspective as well as a tool for describing an emerging form of interaction.

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“…Distributed collaboration of chatbots is a real need. Intent routing, turn taking, context switching, server handling and fulfilling users' satisfactions are the major problems of developing a multi-chatbot system that adopt the distributed collaboration approach [53,54]. In addition, the collaboration feature is implemented with the use of a single orchestration chatbot that orchestrates the conversation and there is not always a direct collaboration between the distributed chatbots.…”

Section: Distributed Collaboration In Multi-chatbot Systemsmentioning

confidence: 99%

Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums

et al. 2021

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Nowadays, museums are developing chatbots to assist their visitors and to provide an enhanced visiting experience. Most of these chatbots do not provide a human-like conversation and fail to deliver the complete requested knowledge by the visitors. There are plenty of stand-alone museum chatbots, developed using a chatbot platform, that provide predefined dialog routes. However, as chatbot platforms are evolving and AI technologies mature, new architectural approaches arise. Museums are already designing chatbots that are trained using machine learning techniques or chatbots connected to knowledge graphs, delivering more intelligent chatbots. This paper is surveying a representative set of developed museum chatbots and platforms for implementing them. More importantly, this paper presents the result of a systematic evaluation approach for evaluating both chatbots and platforms. Furthermore, the paper is introducing a novel approach in developing intelligent chatbots for museums. This approach emphasizes graph-based, distributed, and collaborative multi-chatbot conversational AI systems for museums. The paper accentuates the use of knowledge graphs as the key technology for potentially providing unlimited knowledge to chatbot users, satisfying conversational AI’s need for rich machine-understandable content. In addition, the proposed architecture is designed to deliver an efficient deployment solution where knowledge can be distributed (distributed knowledge graphs) and shared among different chatbots that collaborate when is needed.

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Single or Multiple Conversational Agents?

Cited by 40 publications

References 39 publications

Smells Like Team Spirit

Smells Like Team Spirit

Human-AI interactions through a Gricean lens

Graph-Based Conversational AI: Towards a Distributed and Collaborative Multi-Chatbot Approach for Museums

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