A Need for Trust in Conversational Interface Research

Across several branches of conversational interaction research including interactions with social robots, embodied agents, and conversational assistants, users have identified trust as a critical part of those interactions. Nevertheless, there is little agreement on what trust means within these sort of interactions or how trust can be measured. In this paper, we explore some of the dimensions of trust as it has been understood in previous work and we outline some of the ways trust has been measured in the hopes of furthering discussion of the concept across the field

Computers that smile: Humor in the interface

It is certainly not the case that wen we consider research on the role of human characteristics in the user interface of computers that no attention has been paid to the role of humor. However, when we compare efforts in this area with efforts and experiments that attempt to demonstrate the positive role of general emotion modelling in the user interface, then we must conclude that this attention is still low. As we all know, sometimes the computer is a source of frustration rather than a source of enjoyment. And indeed we see research projects that aim at recognizing a user’s frustration, rather than his enjoyment. However, rather than detecting frustration, and maybe reacting on it in a humorous way, we would like to prevent frustration by making interaction with a computer more natural and more enjoyable. For that reason we are working on multimodal interaction and embodied conversational agents. In the interaction with embodied conversational agents verbal and nonverbal communication are equally important. Multimodal emotion display and detection are among our advanced research issues, and investigations in the role of humor in human-computer interaction is one of them

Chasing the Chatbots: Directions for Interaction and Design Research

Big tech-players have been successful in pushing the chatbots forward. Investments in the technology are growing fast, as well as the number of users and applications available. Instead of driving investments towards a successful diffusion of the technology, user-centred studies are currently chasing the popularity of chatbots. A literature analysis evidences how recent this research topic is, and the predominance of technical challenges rather than understanding users’ perceptions, expectations and contexts of use. Looking for answers to interaction and design questions raised in 2007, when the presence of clever computers in everyday life had been predicted for the year 2020, this paper presents a panorama of the recent literature, revealing gaps and pointing directions for further user-centred research

Challenges in Collaborative HRI for Remote Robot Teams

Collaboration between human supervisors and remote teams of robots is highly challenging, particularly in high-stakes, distant, hazardous locations, such as off-shore energy platforms. In order for these teams of robots to truly be beneficial, they need to be trusted to operate autonomously, performing tasks such as inspection and emergency response, thus reducing the number of personnel placed in harm's way. As remote robots are generally trusted less than robots in close-proximity, we present a solution to instil trust in the operator through a `mediator robot' that can exhibit social skills, alongside sophisticated visualisation techniques. In this position paper, we present general challenges and then take a closer look at one challenge in particular, discussing an initial study, which investigates the relationship between the level of control the supervisor hands over to the mediator robot and how this affects their trust. We show that the supervisor is more likely to have higher trust overall if their initial experience involves handing over control of the emergency situation to the robotic assistant. We discuss this result, here, as well as other challenges and interaction techniques for human-robot collaboration.Comment: 9 pages. Peer reviewed position paper accepted in the CHI 2019 Workshop: The Challenges of Working on Social Robots that Collaborate with People (SIRCHI2019), ACM CHI Conference on Human Factors in Computing Systems, May 2019, Glasgow, U

MIRIAM: A Multimodal Chat-Based Interface for Autonomous Systems

We present MIRIAM (Multimodal Intelligent inteRactIon for Autonomous systeMs), a multimodal interface to support situation awareness of autonomous vehicles through chat-based interaction. The user is able to chat about the vehicle's plan, objectives, previous activities and mission progress. The system is mixed initiative in that it pro-actively sends messages about key events, such as fault warnings. We will demonstrate MIRIAM using SeeByte's SeeTrack command and control interface and Neptune autonomy simulator.Comment: 2 pages, ICMI'17, 19th ACM International Conference on Multimodal Interaction, November 13-17 2017, Glasgow, U

Designing Chatbots for Crises: A Case Study Contrasting Potential and Reality

Chatbots are becoming ubiquitous technologies, and their popularity and adoption are rapidly spreading. The potential of chatbots in engaging people with digital services is fully recognised. However, the reputation of this technology with regards to usefulness and real impact remains rather questionable. Studies that evaluate how people perceive and utilise chatbots are generally lacking. During the last Kenyan elections, we deployed a chatbot on Facebook Messenger to help people submit reports of violence and misconduct experienced in the polling stations. Even though the chatbot was visited by more than 3,000 times, there was a clear mismatch between the users’ perception of the technology and its design. In this paper, we analyse the user interactions and content generated through this application and discuss the challenges and directions for designing more effective chatbots

An End-to-End Conversational Style Matching Agent

We present an end-to-end voice-based conversational agent that is able to engage in naturalistic multi-turn dialogue and align with the interlocutor's conversational style. The system uses a series of deep neural network components for speech recognition, dialogue generation, prosodic analysis and speech synthesis to generate language and prosodic expression with qualities that match those of the user. We conducted a user study (N=30) in which participants talked with the agent for 15 to 20 minutes, resulting in over 8 hours of natural interaction data. Users with high consideration conversational styles reported the agent to be more trustworthy when it matched their conversational style. Whereas, users with high involvement conversational styles were indifferent. Finally, we provide design guidelines for multi-turn dialogue interactions using conversational style adaptation

Social Intelligence Design for Mediated Communication

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