• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.51-58
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• 2010

Since Augmented Reality (AR) enables users to experience computer-generated content embedded in real environments, AR agents can be visualized among physical objects in the environments where the users exist, and directly interact with them in real-time. We model perceptual attention for autonomous agents in AR environments where virtual and physical objects coexist. Since such AR agents must adaptively perceive and attend to surrounded objects relevant to their goals, our model allows the agents to determine currently visible objects from the description of what virtual and physical objects are configured in the camera's viewing area. A degree of attention is assigned to each perceived object based on its relevance to achieve agents' goals. The agents can focus on a reduced set of perceived objects with respect to the estimated degree of attention. To demonstrate the effectiveness of our approach, we implemented an animated character that was overlaid over a miniature version of campus and that attended to buildings relevant to their goals. Experiments showed that our model could reduce the character's perceptual loads even when surroundings change.

• Journal of Convergence for Information Technology
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• v.9 no.2
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• pp.108-114
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• 2019

In this paper, we propose an intelligent vocabulary recommendation agent that automatically provides vocabulary corresponding to game-based learners' needs and requirements in the mobile education augmented reality game environment. The proposed agent reflects the characteristics of mobile technology and augmented reality technology as much as possible. In addition, this agent includes a vocabulary reasoning module, a single game vocabulary recommendation module, a battle game vocabulary recommendation module, a learning vocabulary list Module, and a thesaurus module. As a result, game-based learners' are generally satisfied. The precision of context vocabulary reasoning and thesaurus is 4.01 and 4.11, respectively, which shows that vocabulary related to situation of game-based learner is extracted. However, In the case of satisfaction, battle game vocabulary(3.86) is relatively low compared to single game vocabulary(3.94) because it recommends vocabulary that can be used jointly among recommendation vocabulary of individual learners.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.49-58
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• 2011

Augmented Reality(AR) assists human's cognitive ability through the information visualization by substantiating information about virtual situation. This technology is studied in a variety of ways including education, design, industry, and so on, by various supply of information devices equipped with cameras and display monitors. Since the most of the AR system depends on limited interaction that responds to the order from user, it can not reflect diverse real world situation. In this study, we suggest a knowledge based augmented reality system, which is composed of context awareness agent that provides recognized context information, along with knowledge based component that provides intelligent capability by utilizing domain knowledges. With this capability, the augmented object can generate dynamic model intelligently by reflecting context information, and can make the interaction possible among the multiple objects. We developed rule based context awareness system along with 3D model generation, and tested interaction among the augmented objects. And we suggest a framework that can provide a convenient way of developing augmented reality system for user.