In recent years, game AI technology has made remarkable progress and is still being actively researched. One of the most important technologies for game AI is path finding. Path finding is the calculation of a travel route from a starting point to a destination point. In the field of game AI, path finding methods such as Dijkstra method and A* algorithm are used. Since these methods are suitable for calculating the shortest path, and can be used when an agent wants to track a specific target. However, in a multi-agent environment, where multiple agents are tracking, there is a possibility of overlapping paths between agents. If this problem can be solved and cooperative tracking, such as a pincer movement, can be realized, agents’ movements can be more strategic. The objective of this research is to calculate efficient tracking paths by adjusting the paths of agents so that they do not overlap. The policy of this research is to avoid the vicinity of other agents’ paths so that the agents can choose different paths from each other. Comparison with existing path finding methods shows that the proposed method can avoid overlapping paths and can track efficiently in a pincer movement.

Laser scanning and photogrammetry are the methods to obtain the surface point cloud of an object. Laser scanning can measure the actual size, but some missing spots might appear if the laser does not reach the object surface. In contrast, photogrammetry cannot measure the actual size of an object, but missing spots are less likely to appear. By using laser-scanned point clouds and photogrammetric ones together, it will be possible that point clouds of the object’s actual size will be obtained without any missing surface spots. However, it takes time and effort to perform laser scanning and photogrammetry to the object and synthesize the obtained point clouds. In this paper, an automated measurement system that adds a photogrammetric function to a laser measurement system capable of batch automatic measurement of a large number of objects is proposed. This system makes it possible to efficiently acquire a point cloud of color information included in real size and with no missing points, with as little human intervention as possible. Using this method, we measured the object and confirmed that a significant point cloud could be obtained.

In recent years, there has been an increase in the use of animation as a texture for 3D models in 3DCG and game production. In particular, for some applications, seamlessly repeatable animations are required. Although much research has focused on generating pattern transitions based on physics and chemistry, few methods exist for creating diverse animations that are independent of the input data. In this study, we propose a new animation texture generation method that achieves complex pattern transitions while ensuring seamlessness. By continuously moving the plane area that serves as the cross-section of the voxel data and extracting the cross-sectional image, seamless repeat playback becomes possible. Verification of the proposed method confirmed that seamless animation could be generated under various conditions. Furthermore, it was found that more complex pattern transitions and multiple animation outputs were achievable by extracting cross-sectional images and adjusting the input data.

This study used the Science, Technology, Engineering, Arts, and Mathematics (STEAM) education method to create ‘musical instruments having game-like elements’ for the purpose of investigating the connections and fusion between the arts domain and the sciences domain and to verify the educational effects of said creation. University instructor) and students in the teacher training course cooperatively engaged in instrument-creation activities, and statements of reflection that were recorded by students after their practical training via participation in these creation activities were analyzed. Via the creation of ‘musical instruments having game-like elements’ using STEAM education method, the following educational effects were obtained: Connections and fusion were successfully made between the arts and sciences, student creativity was fostered, and students deepened their understanding of music.

In this paper, we propose a dialogue agent system with a personality presented in a manga-anime style, aiming to facilitate dialogue between humans and manga-anime characters. First, we define the manga-anime style in Japanese Anime, analyze the characteristics of the expression, and design a system that allows a dialogue agent to use Large Language Models (LLMs) to behave like a manga-anime character. The proposed system comprises three main processes: (1) Understanding the user's speech intent using a LLM and dynamically switching character prompts in real-time, thereby reflecting the character's personality in the agent's responses in detail. (2) Determining manga-anime directions such as comic symbols, backgrounds, effects, and composition according to the content of the speech. (3) Generating parameters based on the content and emotion of the speech, which are then linked to the animation of objects that characterize the agent. Using this system, we created an AI character named "Kohane," whose emotional expression changes depending on the conversation with a human. We also conducted comparative experiments with a 3D dialogue agent. As a result, we confirmed that "Kohane," created by the proposed system, gave a stronger impression of being a manga-anime character to participants.