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UnityPGTA: A Unity Platformer Game Testing Automation Tool Using Reinforcement Learning
Se-chan Park, Deock Yeop Kim, Woo Jin Lee
http://doi.org/10.5626/JOK.2024.51.2.149
The cost of game testing in the video game industry is significant, accounting for nearly half of the expenses. Research efforts are underway to automate testing processes to reduce testing costs. However, existing research on test automation often involves manual tasks such as script writing, which is costly and labor-intensive. Additionally, implementations using virtual environments like VGDL and GVG-AI pose challenges when applied to real game testing. In this paper, we propose a tool for automating game testing with the aim of system fault detection, focusing on a Unity platformer game. The proposed tool is based on a commercial game engine, autonomously analyzing the game without human intervention to establish an automated game testing environment. We compare and analyze the error detection results of the proposed tool with a random baseline model using open-source games, demonstrating the tool"s effectiveness in performing automated game analysis and testing environment setup, ultimately reducing testing costs and improving quality and stability.
Community Detection Using Link Attribute-Based Classification
Jeongseon Kim, Soohwan Jeong, Sungsu Lim
http://doi.org/10.5626/JOK.2021.48.8.959
Attempts to discover knowledge through data are becoming gradually diversified to understand a fast and complex world. Graph data analysis, which models and analyzes correlated data as graphs, is drawing much attention as it is combined with the latest machine learning techniques. In this work, we propose a novel methodology for discovering graph community structures. We analyze similarity, curvature-based attributes to allow links existing inside and outside the community to have different attribute values, and exploit them to design and analyze algorithms that eliminate links that affect the community structure less to find better community structures on sparse graphs.
OpenViBE2Unity: Open Source API for Brain-Computer Interface and Unity3D Application
Sooyong Kim, Seongjoon Jeong, Eunmin Lee, Sunghan Lee, Sung Chan Jun, Minkyu Ahn
http://doi.org/10.5626/JOK.2019.46.11.1157
Brain-computer interface, which can quantify a person’s intention, cognition, and feelings, is an active research field. However, building a system that performs well is not a simple task, as it requires data acquisition, real-time processing, and multimodal feedback, and often necessitates considerable cost in time and effort, particularly for untrained users and researchers. Thus, it is important to have an interface that is designed well and works with open platforms, such as Unity3D and OpenViBE that are used widely in BCI development and application. With this goal, we developed OpenViBE2Unity (O2U), an Application Programming Interface that can be used easily for the development of BCI applications. This open API (available through Github) provides special functions that facilitate communication between two popular platforms, OpenViBE and Unity3D. In this paper, we introduce O2U’s architecture and a step-by-step procedure for its application. Finally, we demonstrate one exemplary application developed under O2U.
Implementation of a Stable Point-of-View for Dual Gazing based on the Principle of Eye Movement
Ire Eom, Hanna Lee, Adithya B, Youngho Chai
http://doi.org/10.5626/JOK.2019.46.5.419
Using a dual gaze with eye movement in a first-person game provides a stable view while minimizing blurring of images. The First Person Shooter (FPS) game is based on a first-person viewpoint, since the camera responsible for the player`s gaze and the characters in the game are combined. Thus, when the character moves, the viewpoint moves together, which is the same principle underlying fixed vision with a still head. However, the eyes and the head are separate. Therefore, under multiple eye movements, the head follows the eye after gazing at the object initially, which allows a steady gaze at an object even under body movements.
In this paper, we propose a stable perspective based on the principle of vestibular reflex to the camera in the FPS game. The game environment is created using the Unity game engine, and the stable viewpoint of the dual gaze is demonstrated by comparing the viewpoints when the eye and head are fixed under specific scenarios and during the vestibular reflex.
Reverse Path Activation-based Reverse Influence Maximization in Social Networks
Ashis Talukder, Anupam Kumar Bairagi, Do Hyeon Kim, Choong Seon Hong
http://doi.org/10.5626/JOK.2018.45.11.1203
Influence Maximization (IM) deals with finding influential users for viral marketing in social networks, whereas Reverse Influence Maximization (RIM), a new research direction in the influence-maximization domain, deals with seeding cost, also known as opportunity cost. The IM estimates a small seed set in such a way that by targeting those seed nodes, the influence is maximized in the network. Generally, the seed nodes are assumed to be activated initially in the IM problem. However, we argue that seed nodes need to be influenced by some of their in-neighbor nodes in a similar way how an activated node influences its out-neighbors to be activated. The RIM problem finds the seeding cost, which is defined by the minimum number of nodes that must be activated in order to activate all the seed nodes. In this paper, we propose an Active Reverse Path-based Reverse Influence Maximization (ARP-RIM) model to find the minimum seeding cost. Our model is based on the voting model and the classic Independent Cascade model. We simulate our model with three real datasets of three popular social networks. The experimental result shows that the ARP-RIM model outperforms existing RIM models.
Opinion Classification in Professional Sports Fan Sites using Topic Keyword-Based Sentiment Analysis
Hyungho Byun, Sihyun Jeong, Chong-kwon Kim
http://doi.org/10.5626/JOK.2018.45.4.390
In this study, we propose the classification method using topic keyword-based sentiment analysis through the posts of professional sports fan sites in Korea. We studied ways to take into account the use of special communication methods or vocabulary in the community and defined keywords based on the characteristics of the topic or frequency of the community"s words. In addition, we presented a new sentiment analysis approach that utilizes the use of keyword pools and the proximity relation to keywords. Through three years of actual community dataset, sentiment analysis based on the topic keyword is more effective than the existing method and reflects the community environment.
A Method for Identifying Nicknames of a User based on User Behavior Patterns in an Online Community
http://doi.org/10.5626/JOK.2018.45.2.165
An online community is a virtual group whose members share their interests and hobbies anonymously with nicknames unlike Social Network Services. However, there are malicious user problems such as users who write offensive contents and there may exist data fragmentation problems in which the data of the same user exists in different nicknames. In addition, nicknames are frequently changed in the online community, so it is difficult to identify them. Therefore, in this paper, to remedy these problems we propose a behavior pattern feature vectors for users considering online community characteristics, propose a new implicit behavior pattern called relationship pattern, and identify the nickname of the same user based on Random Forest classifier. Also, Experimental results with the collected real world online community data demonstrate that the proposed behavior pattern and classifier can identify the same users at a meaningful level.
Fast Influence Maximization in Social Networks
Yun-Yong Ko, Kyung-Jae Cho, Sang-Wook Kim
http://doi.org/10.5626/JOK.2017.44.10.1105
Influence maximization (IM) is the problem of finding a seed set composed of k nodes that maximizes the influence spread in social networks. However, one of the biggest problems of existing solutions for IM is that it takes too much time to select a k-seed set. This performance issue occurs at the micro and macro levels. In this paper, we propose a fast hybrid method that addresses two issues at micro and macro levels. Furthermore, we propose a path-based community detection method that helps to select a good seed set. The results of our experiment with four real-world datasets show that the proposed method resolves the two issues at the micro and macro levels and selects a good k-seed set.
A Korean Community-based Question Answering System Using Multiple Machine Learning Methods
Sunjae Kwon, Juae Kim, Sangwoo Kang, Jungyun Seo
Community-based Question Answering system is a system which provides answers for each question from the documents uploaded on web communities. In order to enhance the capacity of question analysis, former methods have developed specific rules suitable for a target region or have applied machine learning to partial processes. However, these methods incur an excessive cost for expanding fields or lead to cases in which system is overfitted for a specific field. This paper proposes a multiple machine learning method which automates the overall process by adapting appropriate machine learning in each procedure for efficient processing of community-based Question Answering system. This system can be divided into question analysis part and answer selection part. The question analysis part consists of the question focus extractor, which analyzes the focused phrases in questions and uses conditional random fields, and the question type classifier, which classifies topics of questions and uses support vector machine. In the answer selection part, the we trains weights that are used by the similarity estimation models through an artificial neural network. Also these are a number of cases in which the results of morphological analysis are not reliable for the data uploaded on web communities. Therefore, we suggest a method that minimizes the impact of morphological analysis by using character features in the stage of question analysis. The proposed system outperforms the former system by showing a Mean Average Precision criteria of 0.765 and R-Precision criteria of 0.872.
Query Expansion based on Word Sense Community
Chang-Uk Kwak, Hee-Geun Yoon, Seong-Bae Park
In order to assist user’s who are in the process of executing a search, a query expansion method suggests keywords that are related to an input query. Recently, several studies have suggested keywords that are identified by finding domains using a clustering method over the documents that are retrieved. However, the clustering method is not relevant when presenting various domains because the number of clusters should be fixed. This paper proposes a method that suggests keywords by finding various domains related to the input queries by using a community detection algorithm. The proposed method extracts words from the top-30 documents of those that are retrieved and builds communities according to the word graph. Then, keywords representing each community are derived, and the represented keywords are used for the query expansion method. In order to evaluate the proposed method, we compared our results to those of two baseline searches performed by the Google search engine and keyword recommendation using TF-IDF in the search results. The results of the evaluation indicate that the proposed method outperforms the baseline with respect to diversity.
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