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We examined the influence of groups of agents and the type of avatar on movement interference. In addition, we studied the synchronization of the subject with the agent. For that, we conducted experiments utilizing human subjects to examine the influence of one, two, or three agents, as well as human or robot avatars, and finally, the agent moving biologically or linearly. We found the main effect on movement interference was the number of agents; namely, three agents had significantly more influence on movement interference than one agent. These results suggest that the number of agents is more influential on movement interference than other avatar characteristics. For the synchronization, the main effect of the type of the agent was revealed, showing that the human agent kept more synchronization compared to the robotic agent. In this experiment, we introduced an additional paradigm on the interference which we called synchronization, discovering that a group of agents is able to influence this behavioral level as well.

Many studies on human–computer interaction have demonstrated that the visual appearance of an agent or a robot significantly influences people’s perceptions and behaviors. Several studies on the appearance of agents/robots have concluded that consistency between expectations from an agent’s or a robot’s appearance and performances was an important factor to the continuous use of these agents/robots. This is because users would stop interacting with the agents/robots when predictions are not met by actual experiences. However, previous studies mainly focused on the consistency between an initial expectation and a performance of a single instance of a task. The influence of the orders of successes or failures for more than one instance of a task has not been examined in detail. Therefore, in this study, we investigate the order effects of how the timing of sufficient or insufficient results of animated agents affects user evaluation. This will lead to the contribution to fill the lack of studies regarding more than one task in the field of human–computer interaction and to realize the continuous use of agents/robots as long as possible and to avoid stopping to use the agents/robots owing to their successful design. We create a simulated retrieval website and conduct an experiment using retrieval assistant agents that show both sufficient and insufficient results for more than one instance of retrieval tasks. The experimental results demonstrated a recency effect wherein the users significantly revised their evaluations of the animated agents based on new information more than that based on initial evaluations. The investigation of the case of repeated instances of a task and the influence of successes or failures is important for designing intelligent agents that may show incomplete results in intelligent tasks. Furthermore, the result of this study will contribute to build strategies to design behaviors of agents/robots that have a high or low evaluation based on their appearance in advance to prevent users from stopping use of the agents/robots.

Touch plays a crucial role in humans’ nonverbal social and affective communication. It then comes as no surprise to observe a considerable effort that has been placed on devising methodologies for automated touch classification. For instance, such an ability allows for the use of smart touch sensors in such real-life application domains as socially-assistive robots and embodied telecommunication. In fact, touch classification literature represents an undeniably progressive result. However, these results are limited in two important ways. First, they are mostly based on overall (i.e., average) accuracy of different classifiers. As a result, they fall short in providing an insight on performance of these approaches as per different types of touch. Second, they do not consider the same type of touch with different level of strength (e.g., gentle versus strong touch). This is certainly an important factor that deserves investigating since the intensity of a touch can utterly transform its meaning (e.g., from an affectionate gesture to a sign of punishment). The current study provides a preliminary investigation of these shortcomings by considering the accuracy of a number of classifiers for both, within- (i.e., same type of touch with differing strengths) and between-touch (i.e., different types of touch) classifications. Our results help verify the strength and shortcoming of different machine learning algorithms for touch classification. They also highlight some of the challenges whose solution concepts can pave the path for integration of touch sensors in such application domains as human–robot interaction (HRI).

This research investigates the effect of different user characteristics on the perception and evaluation of an agent’s appearance variables. Therefore, two different experiments have been conducted. In a 3 × 3 × 5 within-subjects design (Study 1; N = 59), three different target groups (students, elderly, and cognitively impaired people) evaluated 30 different agent appearances that varied in species (human, animal, and robot) and realism (high detail, low detail, stylized shades, stylized proportion, and stylized shade with stylized proportion). Study 2 (N = 792) focused on the effect of moderating variables regarding the same appearance variables and aims to supplement findings of Study 1 based on a 3 × 5 between-subjects design. Results showed effects of species and realism on person perception, users’ liking, and using intention. In a direct comparison, a higher degree of realism was perceived as more positive, while those effects were not replicated in Study 2. Further on, a majority evaluated nonhumanoid agents more positively. Since no interaction effects of species and realism have been found, the effects of stylization seem to equally influence the perception for all kind of species. Moreover, the importance of the target group’s preference was demonstrated, since differences with regard to the appearance evaluation were found.

Multi-agent systems, robotics and software engineering are large and active research areas with many applications in academia and industry. The First Workshop on Agents and Robots for reliable Engineered Autonomy (AREA), organised the first time in 2020, aims at encouraging cross-disciplinary collaborations and exchange of ideas among researchers working in these research areas. This paper presents a perspective of the organisers that aims at highlighting the latest research trends, future directions, challenges, and open problems. It also includes feedback from the discussions held during the AREA workshop. The goal of this perspective is to provide a high-level view of current research trends for researchers that aim at working in the intersection of these research areas.

In recent years, conversational agents (CAs) have become ubiquitous and are a presence in our daily routines. It seems that the technology has finally ripened to advance the use of CAs in various domains, including commercial, healthcare, educational, political, industrial, and personal domains. In this study, the main areas in which CAs are successful are described along with the main technologies that enable the creation of CAs. Capable of conducting ongoing communication with humans, CAs are encountered in natural-language processing, deep learning, and technologies that integrate emotional aspects. The technologies used for the evaluation of CAs and publicly available datasets are outlined. In addition, several areas for future research are identified to address moral and security issues, given the current state of CA-related technological developments. The uniqueness of our review is that an overview of the concepts and building blocks of CAs is provided, and CAs are categorized according to their abilities and main application domains. In addition, the primary tools and datasets that may be useful for the development and evaluation of CAs of different categories are described. Finally, some thoughts and directions for future research are provided, and domains that may benefit from conversational agents are introduced.

Interaction and cooperation with humans are overarching aspirations of artificial intelligence research. Recent studies demonstrate that AI agents trained with deep reinforcement learning are capable of collaborating with humans. These studies primarily evaluate human compatibility through “objective” metrics such as task performance, obscuring potential variation in the levels of trust and subjective preference that different agents garner. To better understand the factors shaping subjective preferences in human-agent cooperation, we train deep reinforcement learning agents in Coins, a two-player social dilemma. We recruit N = 501 participants for a human-agent cooperation study and measure their impressions of the agents they encounter. Participants’ perceptions of warmth and competence predict their stated preferences for different agents, above and beyond objective performance metrics. Drawing inspiration from social science and biology research, we subsequently implement a new “partner choice” framework to elicit revealed preferences: after playing an episode with an agent, participants are asked whether they would like to play the next episode with the same agent or to play alone. As with stated preferences, social perception better predicts participants’ revealed preferences than does objective performance. Given these results, we recommend human-agent interaction researchers routinely incorporate the measurement of social perception and subjective preferences into their studies.

While the research area of artificial intelligence benefited from increasingly sophisticated machine learning techniques in recent years, the resulting systems suffer from a loss of transparency and comprehensibility, especially for end-users. In this paper, we explore the effects of incorporating virtual agents into explainable artificial intelligence (XAI) designs on the perceived trust of end-users. For this purpose, we conducted a user study based on a simple speech recognition system for keyword classification. As a result of this experiment, we found that the integration of virtual agents leads to increased user trust in the XAI system. Furthermore, we found that the user’s trust significantly depends on the modalities that are used within the user-agent interface design. The results of our study show a linear trend where the visual presence of an agent combined with a voice output resulted in greater trust than the output of text or the voice output alone. Additionally, we analysed the participants’ feedback regarding the presented XAI visualisations. We found that increased human-likeness of and interaction with the virtual agent are the two most common mention points on how to improve the proposed XAI interaction design. Based on these results, we discuss current limitations and interesting topics for further research in the field of XAI. Moreover, we present design recommendations for virtual agents in XAI systems for future projects.

The fast propagation of the Internet of Things (IoT) devices has driven to the development of collaborative healthcare frameworks to support the next generation healthcare industry for quality medical healthcare. This paper presents a generalized collaborative framework named collaborative shared healthcare plan (CSHCP) for cognitive health and fitness assessment of people using ambient intelligent application and machine learning techniques. CSHCP provides support for daily physical activity recognition, monitoring, assessment and generate a shared healthcare plan based on collaboration among different stakeholders: doctors, patient guardians, as well as close community circles. The proposed framework shows promising outcomes compared to the existing studies. Furthermore, the proposed framework enhances team communication, coordination, long-term plan management of healthcare information to provide a more efficient and reliable shared healthcare plans to people.

Human operators have the trend of increasing physical and mental workloads when performing teleoperation tasks in uncertain and dynamic environments. In addition, their performances are influenced by subjective factors, potentially leading to operational errors or task failure. Although agent-based methods offer a promising solution to the above problems, the human experience and intelligence are necessary for teleoperation scenarios. In this paper, a truncated quantile critics reinforcement learning-based integrated framework is proposed for human–agent teleoperation that encompasses training, assessment and agent-based arbitration. The proposed framework allows for an expert training agent, a bilateral training and cooperation process to realize the co-optimization of agent and human. It can provide efficient and quantifiable training feedback. Experiments have been conducted to train subjects with the developed algorithm. The performances of human–human and human–agent cooperation modes are also compared. The results have shown that subjects can complete the tasks of reaching and picking and placing with the assistance of an agent in a shorter operational time, with a higher success rate and less workload than human–human cooperation.