Intelligent Virtual Characters Research Articles

A paradigm for assessing adults' and children's concepts of artificially intelligent virtual characters

Children's social networks are filled with real and imagined relationship partners, and advances in artificial intelligence (AI) have generated a host of possible relationships with virtual characters. However, little is known about how children conceptualize virtual characters, and how they might be viewed as friendship partners. These studies were designed to develop a paradigm for investigating children's concepts of virtual characters. In Study 1, items were developed that could be used in a task with children. Adults (N = 144) attributed a range of properties (biological, psychological, and social) to four child agents: a real child, a child on a video chat program, an AI child, and a doll. Items that captured adult concepts were used in a task with children (N = 30; aged 5 to 8 years old). This task was designed to: (a) control for idiosyncratic features of the four child agents, (b) enable the expression of uncertainty, and (c) assess concepts of reciprocal relationships. In Study 1, adults did not differentiate between the AI child and the doll on the social property; however, a subset of adults favored the AI child over the doll for sharing secrets. In Study 2, a subset of children indicated a preference for the doll over the AI child on opportunities for reciprocal love and companionship. These findings suggest that virtual characters afford select social opportunities that may differ between adults and children, raising further questions as to the design goals for virtual characters and the functions they serve in our day-to-day lives.

Development of a computational cognitive architecture for intelligent virtual character

AbstractA development of a computational cognitive architecture for the simulation of intelligent virtual characters is described in this paper. By specializing and adapting from an existing structure for a situated design agent, we propose three process models—reflexive, reactive and reflective—which derive behavioural models that underlie intelligent behaviours for these characters. Various combinations of these process models allow intelligent virtual characters to reason in a reflexive, reactive and/or reflective manner according to the retrieval, modification and reconstruction of their memory contents. This paper offers an infrastructure for combining simple reasoning models, found in crowd simulations, and highly deliberative processing models or reasoning, found in ‘heavy’ agents with high‐level cognitive abilities. Intelligent virtual characters simulated via this adapted architecture can exhibit system level intelligence across a broad range of relevant tasks. To demonstrate the usefulness of the proposed architecture, we describe the effect of the reflexive, reactive and reflective processes on a virtual character in our virtual tour application. Copyright © 2009 John Wiley & Sons, Ltd.

Intelligent virtual humans with autonomy and personality

Intelligent virtual characters has been subject to exponential growth in the last decades and they are utilized in many application areas such as education, training, human-computer interfaces and ...

강화학습과 감정모델 기반의 지능적인 가상 캐릭터의 구현

학습과 감정은 지능형 시스템을 구현하는데 있어 가장 중요한 요소이다. 본 논문에서는 강화학습을 이용하여 사용자와 상호작용을 하면서 학습을 수행하고 내부적인 감정모델을 가지고 있는 지능적인 가상 캐릭터를 구현하였다. 가상 캐릭터는 여러 가지 사물들로 이루어진 3D의 가상 환경 내에서 내부상태에 의해 자율적으로 동작하며, 또한 사용자는 가상 캐릭터에게 반복적인 명령을 통해 원하는 행동을 학습시킬 수 있다. 이러한 명령은 인공신경망을 사용하여 마우스의 제스처를 인식하여 수행할 수 있고 감정의 표현을 위해 Emotion-Mood-Personality 모델을 새로 제안하였다. 그리고 실험을 통해 사용자와 상호작용을 통한 감정의 변화를 살펴보았고 가상 캐릭터의 훈련에 따른 학습이 올바르게 수행되는 것을 확인하였다. Learning and emotions are very important parts to implement intelligent robots. In this paper, we implement intelligent virtual character based on reinforcement learning which interacts with user and have internal emotion model. Virtual character acts autonomously in 3D virtual environment by internal state. And user can learn virtual character specific behaviors by repeated directions. Mouse gesture is used to perceive such directions based on artificial neural network. Emotion-Mood-Personality model is proposed to express emotions. And we examine the change of emotion and learning behaviors when virtual character interact with user.