Biblio

Whether an idea, information, disease, or innovation diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. Recent studies have shown that diffusion can fail on a network in which people are only active in "bursts," active for a while and then silent for a while, but diffusion could succeed on the same network if people were active in a more random Poisson manner. Those studies generally consider models in which nodes are active according to the same random timing process and then ask which timing is optimal. In reality, people differ widely in their activity patterns – some are bursty and others are not. We model diffusion on networks in which agents differ in their activity patterns. We show that bursty behavior does not always hurt the diffusion, and in fact having some (but not all) of the population be bursty significantly helps diffusion. We prove that maximizing diffusion requires heterogeneous activity patterns across agents, and the overall maximizing pattern of agents' activity times does not involve any Poisson behavior.

In this paper we discuss building large scale virtual reality reconstructions of historical heritage sites and populating it with crowds of virtual agents. Such agents are capable of performing complex actions, while respecting the cultural and historical accuracy of agent behaviour. In many commercial video games such agents either have very limited range of actions (resulting primitive behaviour) or are manually designed (resulting high development costs). In contrast, we follow the principles of automatic goal generation and automatic planning. Automatic goal generation in our approach is achieved through simulating agent needs and then producing a goal in response to those needs that require satisfaction. Automatic planning refers to techniques that are concerned with producing sequences of actions that can successfully change the state of an agent to the state where its goals are satisfied. Classical planning algorithms are computationally costly and it is difficult to achieve real-time performance for our problem domain with those. We explain how real-time performance can be achieved with Case-Based Planning, where agents build plan libraries and learn how to reuse and combine existing plans to archive their dynamically changing goals. We illustrate the novelty of our approach, its complexity and associated performance gains through a case-study focused on developing a virtual reality reconstruction of an ancient Mesopotamian settlement in 5000 B.C.

Nonverbal cues are considered the most important part in social communication. Many people desire people; but due to the stigma and unavailability of resources, they are unable to practice their social skills. In this work, we envision a virtual assistant that can give individuals real-time feedback on their smiles, eye-contact, body language and volume modulation that is available anytime, anywhere using a computer browser. To instantiate our idea, we have set up a Wizard-of-Oz study in the context of speed-dating with 47 individuals. We collected videos of the participants having a conversation with a virtual agent before and after of a speed-dating session. This study revealed that the participants who used our system improved their gesture in a face-to-face conversation. Our next goal is to explore different machine learning techniques on the facial and prosodic features to automatically generate feedback on the nonverbal cues. In addition, we want to explore different strategies of conveying real-time feedback that is non-threatening, repeatable, objective and more likely to transfer to a real-world conversation.

In interpersonal interactions, humans speak in part by considering their social distance and position with respect to other people, thereby developing relationships. In our research, we focus on positive politeness (PP), a strategy for positively reducing the distance people in human communication using language. In addition, we propose an agent that attempts to actively interact with humans. First, we design a dialog system based on the politeness theory. Next, we examine the effect of our proposed method on interactions. For our experiments, we implemented two agents:the method proposed for performing PP and a conventional method that performs negative politeness based on the unobjectionable behavior. We then compare and analyze impressions of experiment participants in response to the two agents. From our results, male participants accepted PP more frequently than female participants. Further, the proposed method lowered the perceived sense of interacting with a machine for male participants.

For optimal human-robot interaction, understanding the determinants and components of anthropomorphism is crucial. This research assessed the influence of an agent's social cues and controlling language use on user's perceptions of the agent's expertise, sociability, and trustworthiness. In a game context, the agent attempted to persuade users to modify their choices using high or low controlling language and using different levels of social cues (advice with text-only with no robot embodiment as the agent, a robot with elementary social cues, and a robot with advanced social cues). As expected, low controlling language lead to higher perceived anthropomorphism, while the robotic agent with the most social cues was selected as the most expert advisor and the non-social agent as the most trusted advisor.

With increasing interest in the development of intelligent agents capable of learning, proficiently automating tasks, and gaining world knowledge, the importance of integrating the ability to converse naturally with users is more crucial now than ever before. This thesis aims to understand and characterize different aspects of social language to facilitate the development of intelligent agents that are socially aware and able to engage users to a level that was not previously possible with language generation systems. Using various machine learning algorithms and data-driven approaches to model the nuances of social language in dialogue, such as factual and emotional expression, sarcasm and humor and the related subclasses of rhetorical questions and hyperbole, we can come closer to modeling the characteristics of the social language that allows us to express emotion and knowledge, and thereby exhibit these styles in the agents we develop.

This paper describes a small experimental study into the use of avatars to remediate the lecturer's absence in voice-over-slide material. Four different avatar behaviours are tested. Avatar A performs all the upper-body gestures of the lecturer, which were recorded using a 3D depth sensor. Avatar B is animated using few random gestures in order to create a natural presence that is unrelated to the speech. Avatar C only performs the lecturer's pointing gestures, as these are known to indicate important parts of a lecture. Finally, Avatar D performs "lecturer-like" gestures, but these are desynchronised with the speech. Preliminary results indicate students' preference for Avatars A and C. Although the effect of avatar behaviour on learning did not prove statistically significant, students' comments indicate that an avatar that behaves quietly and only performs some of the lecturer's gestures (pointing) is effective. The paper also presents a simple empirical method for automatically detecting pointing gestures in Kinect recorded lecture data.

Never Alone (2016) is a generative large-scale urban screen video-sound installation, which presents the idea of generative choreographies amongst multiple video agents, or "digital performers". This generative installation questions how we navigate in urban spaces and the ubiquity and disruptive nature of encounters within the cities' landscapes. The video agents explore precarious movement paths along the façade inhabiting landscapes that are both architectural and emotional.

In this study, we used a humanoid robot as a telepresence robot and compared with the basic telepresence robot which can only rotate the display in order to reveal the effect of embodiment. We also investigated the effect caused by changing the body size of the humanoid robot by using two different size of robots. Our experimental results revealed that the embodiment increases the remote person's social telepresence, familiarity, and directivity. The comparison between small and big humanoid robots showed no difference and both of them were effective.

Clickjacking attacks are emerging threats to websites of different sizes and shapes. They are particularly used by threat agents to get more likes and/or followers in Online Social Networks (OSNs). This paper reviews the clickjacking attacks and the classic solutions to tackle various forms of those attacks. Different approaches of Cross-Site Scripting attacks are implemented in this study to study the attack tools and methods. Various iFrame attacks have been developed to tamper with the integrity of the website interactions at the application layer. By visually demonstrating the attacks such as Cross-Site scripting (XSS) and Cross-Site Request Forgery (CSRF), users will be able to have a better understanding of such attacks in their formulation and the risks associated with them.

In this paper, we present E-VOX, an emotionally enhanced semantic ECA designed to work as a virtual assistant to search information from Wikipedia. It includes a cognitive-affective architecture that integrates an emotion model based on ALMA and the Soar cognitive architecture. This allows the ECA to take into account features needed for social interaction such as learning and emotion management. The architecture makes it possible to influence and modify the behavior of the agent depending on the feedback received from the user and other information from the environment, allowing the ECA to achieve a more realistic and believable interaction with the user. A completely functional prototype has been developed showing the feasibility of our approach.

Social and emotional intelligence of computer systems is increasingly important in human-AI (Artificial Intelligence) interactions. This paper presents a tangible AI interface, T.A.I, that enhances physical engagement in digital communication between users and a conversational AI agent. We describe a compact, pneumatically shape-changing hardware design with a rich set of physical gestures that actuate on mobile devices during real-time conversations. Our user study suggests that the physical presence provided by T.A.I increased users' empathy for, and social connection with the virtual intelligent system, leading to an improved Human-AI communication experience.

This paper contributes a systematic research approach as well as findings of an empirical study conducted to investigate the effect of virtual agents on task performance and player experience in digital games. As virtual agents are supposed to evoke social effects similar to real humans under certain conditions, the basic social phenomenon social facilitation is examined in a testbed game that was specifically developed to enable systematical variation of single impact factors of social facilitation. Independent variables were the presence of a virtual agent (present vs. not present) and the output device (ordinary monitor vs. head-mounted display). Results indicate social inhibition effects, but only for players using a head-mounted display. Additional potential impact factors and future research directions are discussed.

The goal of this work is to model a virtual character able to converse with different interpersonal attitudes. To build our model, we rely on the analysis of multimodal corpora of non-verbal behaviors. The interpretation of these behaviors depends on how they are sequenced (order) and distributed over time. To encompass the dynamics of non-verbal signals across both modalities and time, we make use of temporal sequence mining. Specifically, we propose a new algorithm for temporal sequence extraction. We apply our algorithm to extract temporal patterns of non-verbal behaviors expressing interpersonal attitudes from a corpus of job interviews. We demonstrate the efficiency of our algorithm in terms of significant accuracy improvement over the state-of-the-art algorithms.

The design of systems with independent agency to act on the environment or which can act as persuasive agents requires consideration of not only the technical aspects of design, but of the psychological, sociological, and philosophical aspects as well. Creating usable, safe, and ethical systems will require research into human-computer communication, in order to design systems that can create and maintain a relationship with users, explain their workings, and act in the best interests of both users and of the larger society.

Personal agent software is now in daily use in personal devices and in some organizational settings. While many advocate an agent sociality design paradigm that incorporates human-like features and social dialogues, it is unclear whether this is a good match for professionals who seek productivity instead of leisurely use. We conducted a 17-day field study of a prototype of a personal AI agent that helps employees find work-related information. Using log data, surveys, and interviews, we found individual differences in the preference for humanized social interactions (social-agent orientation), which led to different user needs and requirements for agent design. We also explored the effect of agent proactive interactions and found that they carried the risk of interruption, especially for users who were generally averse to interruptions at work. Further, we found that user differences in social-agent orientation and aversion to agent proactive interactions can be inferred from behavioral signals. Our results inform research into social agent design, proactive agent interaction, and personalization of AI agents.