Similar to relationships between humans, a person desiring to form a good relationship with a robot needs to be able to empathize with it. However, the specific kinds of human-robot interactions that would arouse and enhance empathy for the robot in the user's mind have not yet been clarified. In addition, the human behavioral traits that may be regarded as indices of empathy have not been investigated extensively. In an attempt to address these two issues, a preliminary experiment on empathy in human-robot interaction is conducted. The results suggest that the actions of naming or comforting a robot could contribute to enhancing its user's empathy and that eye fixation could be used as an index of empathy even when the use of a subjective index is inconclusive.

The task of automated risk assessment is attracting significant attention in the light of the recent microloan popularity growth. The industry requires a real time method for the timely processing of the extensive number of applicants for short-term small loans. Owing to the vast number of applications, manual verification is not a viable option. In cooperation with a microloan company in Azerbaijan, we have researched automated risk assessment using crowdsourcing. The principal concept behind this approach is the fact that a significant amount of information relating to a particular applicant can be retrieved from the social networks. The suggested approach can be divided into three parts: First, applicant information is collected on social networks such as LinkedIn and Facebook. This can only occur with the applicant's permission. Then, this data is processed using a program that extracts the relevant information segments. Finally, these information segments are evaluated using crowdsourcing. We attempted to evaluate the information segments using social networks. To that end, we automatically posted requests on the social networks regarding certain information segments and evaluated the community response by counting “likes” and “shares”. For example, we posted the status, “Do you think that a person who has worked at ABC Company is more likely to repay a loan? Please “like” this post if you agree.” From the results, we were able to estimate public opinion. Once evaluated, each information segment was then given a weight factor that was optimized using available loan-repay test data provided to us by a company. We then tested the proposed system on a set of 400 applicants. Using a second crowdsourcing approach, we were able to confirm that the resulting solution provided a 92.5% correct assessment, with 6.45% false positives and 11.11% false negatives, with an assessment duration of 24 hours.

Learning is essential for an autonomous agent to adapt to an environment. One method that can be used is learning through trial and error. However, it is impractical because of the long learning time required when the agent learns in a complex environment. Therefore, some guidelines are necessary to expedite the learning process in a complex environment. Imitation of the behavior of other agents who have already adapted to the environment would shorten an agent's learning time. Thus, imitation can be used by agents as a guideline for learning. In this study, we propose a computational model of imitation and autonomous behavior. We expect that an agent can reduce its learning time through imitation. The actions that an agent performs are represented by a set of features such as the type, location, and object of an action. The agent tends to imitate the similar actions of other agents, and the similarity between actions is calculated, which is indicative of the importance of each feature. The proposed model is evaluated using a dining table simulator. The experimental results indicate that the proposed model can adapt to the environment faster than a baseline model that learns only through trial and error, and that the proposed model can shorten the learning time further if the importance of each feature can be adjusted by learning.

When talking to infants, we tend to use infant-directed speech (IDS) rather than adult-directed speech (ADS). IDS attracts more attention from infants, conveys the emotions of adults more easily, and makes language acquisition easier. It is not clear, however, whether IDS has a cognitive effect on adults as well. To address this issue, we focus on one of the most distinctive features of IDS, a high-pitched voice. In addition, we conduct two human-robot interaction experiments to examine the following two hypotheses: (i) a robot reacting selectively to a high-pitched voice triggers a high-pitched voice of the user (H1), and (ii) talking to a robot in a high-pitched voice improves the user's impression of the robot (H2). The results did not support H1, but marginally supported H2.

Multi-Agent Systems have been in the focus of research interest for a long time. These systems are used widely in such areas as logistics, management, various simulations, game development and many others. In this paper, we search for a method to increase flexibility and efficiency of Multi-Agent Systems by investigating the usefulness of Decision Support Systems for intelligent agents in multi-agent environment. We also research advantages, such as the ability to add new agents at run-time, and disadvantages, such as increased complexity, of creating and modifying Decision Support Systems dynamically in Multi-Agent System.

If robots learn new actions through human-robot interaction, it is important that the robots can utilize rewards as well as instructions to reduce humans' efforts. Additioanlly, "interval" which allows humans to give instructions and evaluations is also important. We hence focused on "delays in initiating actions" and changed them according to the progress of learning: long delays at early stages, and short at later stages. We compared the proposed varying delay with a constant delay by an experiment. The result demonstrated that the varying delay improves learning efficiency significantly and impresses humans as teachable.

Human interaction is one of the most important characteristics of group social dynamics in meetings. In this paper, we propose an approach for capture, recognition, and visualization of human interactions. Unlike physical interactions (e.g., turn-taking and addressing), the human interactions considered here are incorporated with semantics, i.e., user intention or attitude toward a topic. We adopt a collaborative approach for capturing interactions by employing multiple sensors, such as video cameras, microphones, and motion sensors. A multimodal method is proposed for interaction recognition based on a variety of contexts, including head gestures, attention from others, speech tone, speaking time, interaction occasion (spontaneous or reactive), and information about the previous interaction. A support vector machines (SVM) classifier is used to classify human interaction based on these features. A graphical user interface called MMBrowser is presented for interaction visualization. Experimental results have shown the effectiveness of our approach.

In this paper, we describe the enabling technologies to develop a smart meeting system based on a three layered generic model. From physical level to semantic level, it consists of meeting capturing, meeting recognition, and semantic processing. Based on the overview of underlying technologies and existing work, we propose a novel real-world smart meeting application, called MeetingAssistant. It is distinctive from previous systems in two aspects. First it provides the real-time browsing that allows a participant to instantly view the status of the current meeting. This feature is helpful in activating discussion and facilitating human communication during a meeting. Second, the context-aware browsing adaptively selects and displays meeting information according to user's situational context, e.g., user purpose, which makes meeting viewing more efficient.

In this paper, we discuss a user interface issue with regard to automatic video editing based on the speaker's intentions. In our experiments, the subjects used previously developed video capturing system to employ 4 types of editing methods by making 3 types of presentations. Subjective evaluation revealed that the editing method that used behaviors-for-attention obtained a good score for a presentation in which the subject was provided with specific instructions regarding the tasks to be performed. In the case of a presentation without a scenario, an editing method using a footswitch and a posture obtained a higher score. It can be concluded that a combination of both behavior-based and footswitch-based editing would provide a good environment for content acquisition

In desktop manipulations such as cooking or assembly instruction videos, a performer often draws the viewers' attention to significant portions of the video's content by means of a specific set of physical behaviors. We verified the efficacy of videos edited based on those behaviors by observing television programs and simulating them with the use of our editing scheme. We first discuss the typical behaviors commonly shown in TV programs, and statistically describe the relationships between certain types of shot changes. We then propose an editing method using those behaviors, and demonstrate that our method generates videos whose quality is close to that of some of television programs. Moreover, we demonstrate the usability of our online editing system in experiments involving actual scenes.

In this paper, we introduce an intelligent system for video recording. First, we categorized targets and purposes of shooting, and discuss the cameraworks appropriate for them. Then, we propose camera control algorithms to realize such cameraworks. Based on this idea, we built a prototype pan-tilt camera control system, in which multiple cameras with different purposes automatically track and shoot the targets. We evaluated our system through recording of some presentations on desktop manipulation. The effectiveness of our algorithm was verified through some experiments.