Designing friends

Embodied Conversational Agents are virtual humans that can interact with humans using verbal and non-verbal forms of communication. In most cases, they have been designed for short interactions. This paper asks the question how one would start to design synthetic characters that can become your friends. We look at insights from social psychology and propose a methodology for designing friends

Chatbots for learning: A review of educational chatbots for the Facebook Messenger

With the exponential growth in the mobile device market over the last decade, chatbots are becoming an increasingly popular option to interact with users, and their popularity and adoption are rapidly spreading. These mobile devices change the way we communicate and allow ever-present learning in various environments. This study examined educational chatbots for Facebook Messenger to support learning. The independent web directory was screened to assess chatbots for this study resulting in the identification of 89 unique chatbots. Each chatbot was classified by language, subject matter and developer's platform. Finally, we evaluated 47 educational chatbots using the Facebook Messenger platform based on the analytic hierarchy process against the quality attributes of teaching, humanity, affect, and accessibility. We found that educational chatbots on the Facebook Messenger platform vary from the basic level of sending personalized messages to recommending learning content. Results show that chatbots which are part of the instant messaging application are still in its early stages to become artificial intelligence teaching assistants. The findings provide tips for teachers to integrate chatbots into classroom practice and advice what types of chatbots they can try out.Web of Science151art. no. 10386

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

No Grice: Computers that Lie, Deceive and Conceal

In the future our daily life interactions with other people, with computers, robots and smart environments will be recorded and interpreted by computers or embedded intelligence in environments, furniture, robots, displays, and wearables. These sensors record our activities, our behavior, and our interactions. Fusion of such information and reasoning about such information makes it possible, using computational models of human behavior and activities, to provide context- and person-aware interpretations of human behavior and activities, including determination of attitudes, moods, and emotions. Sensors include cameras, microphones, eye trackers, position and proximity sensors, tactile or smell sensors, et cetera. Sensors can be embedded in an environment, but they can also move around, for example, if they are part of a mobile social robot or if they are part of devices we carry around or are embedded in our clothes or body. \ud \ud Our daily life behavior and daily life interactions are recorded and interpreted. How can we use such environments and how can such environments use us? Do we always want to cooperate with these environments; do these environments always want to cooperate with us? In this paper we argue that there are many reasons that users or rather human partners of these environments do want to keep information about their intentions and their emotions hidden from these smart environments. On the other hand, their artificial interaction partner may have similar reasons to not give away all information they have or to treat their human partner as an opponent rather than someone that has to be supported by smart technology.\ud \ud This will be elaborated in this paper. We will survey examples of human-computer interactions where there is not necessarily a goal to be explicit about intentions and feelings. In subsequent sections we will look at (1) the computer as a conversational partner, (2) the computer as a butler or diary companion, (3) the computer as a teacher or a trainer, acting in a virtual training environment (a serious game), (4) sports applications (that are not necessarily different from serious game or education environments), and games and entertainment applications

Experimenting with the Gaze of a Conversational Agent

We have carried out a pilot experiment to investigate the effects of different eye gaze behaviors of a cartoon-like talking face on the quality of human-agent dialogues. We compared a version of the talking face that roughly implements some patterns of humanlike behavior with two other versions. We called this the optimal version. In one of the other versions the shifts in gaze were kept minimal and in the other version the shifts would occur randomly. The talking face has a number of restrictions. There is no speech recognition, so questions and replies have to\ud be typed in by the users of the systems. Despite this restriction we found that participants that conversed with the optimal agent appreciated the agent more than participants that conversed with the other agents. Conversations with the optimal version proceeded more efficiently. Participants needed less time to complete their task

How the agent’s gender influence users’ evaluation of a QA system

In this paper we present the results of a pilot study investigating the effects of agents’ gender-ambiguous vs. gender-marked look on the perceived interaction quality of a multimodal question answering system. Eight test subjects interacted with three system agents, each having a feminine, masculine or gender-ambiguous look. The subjects were told each agent was representing a differently configured system. In fact, they were interacting with the same system. In the end, the subjects filled in an evaluation questionnaire and participated in an in-depth qualitative interview. The results showed that the user evaluation seemed to be influenced by the agent’s gender look: the system represented by the feminine agent achieved on average the highest evaluation scores. On the other hand, the system represented by the gender-ambiguous agent was systematically lower rated. This outcome might be relevant for an appropriate agent look, especially since many designers tend to develop gender-ambiguous characters for interactive interfaces to match various users’ preferences. However, additional empirical evidence is needed in the future to confirm our findings