We provide an initial model and preliminary findings of a lookahead based local optimization scheme for collision resolution between agents in large goal-directed crowd simulations. Considering crowd simulation to be a global optimization problem, we break down this large problem into smaller problems where each potential collision resolution step is independently optimized in terms of a criticality measure. Agents resolved earlier in order of criticality, maintain the optimized velocity obtained, for the resolution of agents that come later in that order. Hence, the problem is converted to a low dimensional optimization problem of one or two agents where all other obstacles are static or deterministically dynamic. We illustrate the performance of our method on four well known test scenarios.

We provide an initial model and preliminary findings of a lookahead based local optimization scheme for collision resolution between agents in large goal-directed crowd simulations. Considering crowd simulation to be a global optimization problem, we break down this large problem into smaller problems where each potential collision resolution step is independently optimized in terms of a criticality measure. Agents resolved earlier in order of criticality, maintain the optimized velocity obtained, for the resolution of agents that come later in that order. Hence, the problem is converted to a low dimensional optimization problem of one or two agents where all other obstacles are static or deterministically dynamic. We illustrate the performance of our method on four well known test scenarios.

In order to design social agents for long term interactions, it is important to enable them to adapt to the users. In this paper, we chose personality as a medium for adaptation. We conducted a study with 20 participants who watched a story presented by a virtual character in one of two conditions: extroverted or introverted. The study aimed at assessing the impacts of matching the personality of the user with the virtual character through body language on the likability of the character and the information recall of the story. Our findings do not appear to coincide with theoretical expectations since the extroverted character had higher ratings of likability regardless of the personality of the user. Results have also shown a marginal positive effect of the encounter with the introverted character in terms of memory recall. We discuss the important implications that these results may have in the future for human agent interaction design.

Agent-based crowd simulation in virtual environments is of great utility in a variety of domains, from the entertainment industry to serious applications including mobile robots and swarms. Many studies of crowd behavior simulations do not consider the fact that people tend to congregate in smaller social gatherings, such as friends, or families, rather than walking alone. Based on a real-time crowd simulator which has been implemented as a unilateral incompressible fluid and augmented with group behaviors, a perceptual study was conducted to determine the impact of groups on the perception of the crowds at various densities from different camera views. If it is not possible to see groups under certain circumstances, then it may not be necessary to simulate them, to reduce the amount of calculations, an important issue in real-time simulations. This study provides researchers with a proper reference to design better algorithms to simulate realistic behaviors.

Mixed reality offers new potentials for social interaction experiences with virtual agents. In addition, it can be used to experiment with the design of physical robots. However, while previous studies have investigated comfortable social distances between humans and artificial agents in real and virtual environments, there is little data with regards to mixed reality environments. In this paper, we conducted an experiment in which participants were asked to walk up to an agent to ask a question, in order to investigate the social distances maintained, as well as the subject's experience of the interaction. We manipulated both the embodiment of the agent (robot vs. human and virtual vs. physical) as well as closed vs. open posture of the agent. The virtual agent was displayed using a mixed reality headset. Our experiment involved 35 participants in a within-subject design. We show that, in the context of social interactions, mixed reality fares well against physical environments, and robots fare well against humans, barring a few technical challenges.

Mixed reality environments offer new potentials for the design of compelling social interaction experiences with virtual characters. In this paper, we summarise initial experiments we are conducting in which we measure comfortable social distances between humans, virtual humans and virtual robots in mixed reality environments. We consider a scenario in which participants walk within a comfortable distance of a virtual character that has its appearance varied between a male and female human, and a standard- and human-height virtual Pepper robot. Our studies in mixed reality thus far indicate that humans adopt social zones with artificial agents that are similar in manner to human-human social interactions and interactions in virtual reality.

Modelling and simulation of pedestrian crowds require agents to reach pre-determined goals and avoid collisions with static obstacles and dynamic pedestrians, while maintaining natural gait behaviour. We model pedestrians as autonomous, learning, and reactive agents employing Reinforcement Learning (RL). Typical RL-based agent simulations suffer poor generalization due to handcrafted reward function to ensure realistic behaviour. In this work, we model pedestrians in a modular framework integrating navigation and collision-avoidance tasks as separate modules. Each such module consists of independent state-spaces and rewards, but with shared action-spaces. Empirical results suggest that such modular framework learning models can show satisfactory performance without tuning parameters, and we compare it with the state-of-art crowd simulation methods.

Understanding emotional states is a challenging task which frequently leads to misinterpretation even in human observers. While the perception of emotions has been studied extensively in human psychology, little is known about what factors influence the human perception of emotions in robots and virtual characters. In this paper, we build on the Brunswik lens model to investigate the influence of (a) the agent's embodiment using a 2D virtual character, a 3D blended embodiment, a recording of the 3D platform and a recording of a human, as well as (b) the level of human-likeness on people's ability to interpret emotional facial expressions in an agent. In addition, we measure social traits of the human observers and analyze how they correlate to the success in recognizing emotional expressions. We find that interpersonal differences play a minor role in the perception of emotional states. However, both embodiment and human-likeness as well as related perceptual dimensions such as perceived social presence and uncanniness have an effect on the attribution of emotional states.

This work explores the relationship between a robot's embodiment and people's ability to mimic its behavior. It presents a study in which participants were asked to mimic a 3D mixed-embodied robotic head and a 2D version of the same character. Quantitative and qualitative analysis were performed from questionnaires. Quantitative results show no significant influence of the character's embodiment on the self-assessed ability to mimic it, while qualitative ones indicate a preference for mimicking the robotic head.

Virtual characters are an integral part of many game and learning environments and have practical applications as tutors, demonstrators or even representations of the user. However, creating virtual character behaviors can be a time-consuming and complex task requiring substantial technical expertise. To accelerate and better enable the use of virtual characters in social games, we present a virtual character behavior toolkit for the development of expressive virtual characters. It is a midlleware toolkit which sits on top of the game engine with a focus on providing high-level character behaviors to quickly create social games. The toolkit can be adapted to a wide range of scenarios related to social interactions with individuals and groups at multiple distances in the virtual environment and supports customization and control of facial expressions, body animations and group formations. We describe the design of the toolkit, providing an examplar of a small game that is being created with it and our intended future work on the system.