Research on virtual characters has been ongoing for the past 20 years. Early efforts focused mostly on making the characters move and speak-that is, on body and facial animation. Simultaneously, researchers worked on making characters look convincing by adding animation and rendering hair, clothes, and muscles. The next step was to increase artists' interactive control over characters so that it was easier to create convincing video games and cinema. Today, research into user interactivity has come to the forefront. It's no longer sufficient for characters to simply look like imitations of humans. They must behave like humans, too. This fact drives research into emotional and conversational virtual characters, or embodied conversational agents. The goal is to create a virtual character that has a human-like personality and that can emotionally respond while conversing with a user. To this end, some researchers mathematically model emotions, behavior, mood, and personality for virtual characters. As we describe here, researchers can use these models to create an emotionally responsive character. However, such models lack the critical component of memory-a memory of not just events but also past emotional interaction.We've developed a memory-based emotion model that uses the memory of past interactions to build long-term relationships between the virtual character and users. We combine this model with stateof-the-art animation blending to generate smooth animation for the character during the interaction. To make the interaction more natural, we also use face recognition techniques; the character can thus "remember" a user's face and automatically adjust the current interaction on the basis of its existing relationship with the user. Finally, to increase the user's immersion, we place a life-sized character in a real environment using marker-based augmented reality (AR) techniques. Our example application is Eva, a geography teacher who has multiple interactions with two student users. Modeling Realistic CharactersTo create realistic characters, we must create models based on three general aspects: emotion, mood and personality, and relationship. Modeling EmotionsEmotions have proven effects on cognitive processes such as action selection, learning, memory, motivation, and planning. Our emotions both motivate our decisions and have impact on our actions. As such, they're a key mechanism for controlling virtual-character behavior by both creating characters' personality and automatically producing animations by simulating characters' internal dynamics.Jonathan Gratch and Stacy Marsella define two methods for modeling emotion in lifelike characters: communicative-driven methods and simulation-based methods.1 Communicative-driven methods treat emotional displays as a means of communication. These systems don't internally calculate emotion; instead, they select an emoThe search for the perfect virtual character is on, but the moment users interact with characters, any illusion that we've found it is broken. Adding mem...

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With the recent advances, today people are able to communicate with embodied (virtual/robotic) entities using natural ways of communication. In order to use them in our daily lives, they need to be intelligent enough to make long-term relationships with us and this is highly challenging. Previous work on long-term interaction frequently reported that after the novelty effect disappeared, users' interest into the interaction decreased with time. Our primary goal in this study was to develop a system that can still keep the attention of the users after the first interaction.Incorporating the notion of time, we think that the key to long-term interaction is the recall of past memories during current conversation. For this purpose, we developed a longterm interaction framework with remembering and dialogue planning capability. In order to see the effect of remembering on users, we designed a tutoring application and measured the changes in social presence and task engagement levels according to the existence of memory. Different from previous work, users' interest in our system did not decrease with time with the important contributions of remembering to the engagement level of users.