• Title/Summary/Keyword: Learning-based game model

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PBSL(Project based Self Learning) for Pre-production of Game·Animation·Visual Images (게임·애니메이션·영상 기획 프로젝트 수업을 위한 PBSL(Project based Self Learning))

  • Lee, Hyun-Seok
    • Journal of Digital Convergence
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    • v.17 no.11
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    • pp.467-474
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    • 2019
  • Key areas of digital contents, the games and animation industries are increasingly expanding. Therefore, training of a specialized workforce is required in accordance with these enterprises' growing demand. Education in the field of games and animation lies in cultivating talents with creative thinking, collaboration, and problem-solving skills. Thus, this paper aims to propose a PBSL teaching model for creative convergent talent through game and animation projects. The study will focus on the characteristics of creative convergence talents, project teaching, and related job competencies for game and animation education. Based on literature research, a 'Project Based Self Learning' instructional model is presented, in which creative thinking and collaboration competencies are explained in a way they can be performed by the learner. As a case study, D University's class was applied with PBSL. A survey showed that the autonomy aspects were higher than the creativity and convergence attitudes, indicating that the students improved their autonomy and motivation. However, the team composition needs further supplementation.

Chatting Pattern Based Game BOT Detection: Do They Talk Like Us?

  • Kang, Ah Reum;Kim, Huy Kang;Woo, Jiyoung
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.11
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    • pp.2866-2879
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    • 2012
  • Among the various security threats in online games, the use of game bots is the most serious problem. Previous studies on game bot detection have proposed many methods to find out discriminable behaviors of bots from humans based on the fact that a bot's playing pattern is different from that of a human. In this paper, we look at the chatting data that reflects gamers' communication patterns and propose a communication pattern analysis framework for online game bot detection. In massive multi-user online role playing games (MMORPGs), game bots use chatting message in a different way from normal users. We derive four features; a network feature, a descriptive feature, a diversity feature and a text feature. To measure the diversity of communication patterns, we propose lightly summarized indices, which are computationally inexpensive and intuitive. For text features, we derive lexical, syntactic and semantic features from chatting contents using text mining techniques. To build the learning model for game bot detection, we test and compare three classification models: the random forest, logistic regression and lazy learning. We apply the proposed framework to AION operated by NCsoft, a leading online game company in Korea. As a result of our experiments, we found that the random forest outperforms the logistic regression and lazy learning. The model that employs the entire feature sets gives the highest performance with a precision value of 0.893 and a recall value of 0.965.

A Naive Bayesian-based Model of the Opponent's Policy for Efficient Multiagent Reinforcement Learning (효율적인 멀티 에이전트 강화 학습을 위한 나이브 베이지만 기반 상대 정책 모델)

  • Kwon, Ki-Duk
    • Journal of Internet Computing and Services
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    • v.9 no.6
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    • pp.165-177
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    • 2008
  • An important issue in Multiagent reinforcement learning is how an agent should learn its optimal policy in a dynamic environment where there exist other agents able to influence its own performance. Most previous works for Multiagent reinforcement learning tend to apply single-agent reinforcement learning techniques without any extensions or require some unrealistic assumptions even though they use explicit models of other agents. In this paper, a Naive Bayesian based policy model of the opponent agent is introduced and then the Multiagent reinforcement learning method using this model is explained. Unlike previous works, the proposed Multiagent reinforcement learning method utilizes the Naive Bayesian based policy model, not the Q function model of the opponent agent. Moreover, this learning method can improve learning efficiency by using a simpler one than other richer but time-consuming policy models such as Finite State Machines(FSM) and Markov chains. In this paper, the Cat and Mouse game is introduced as an adversarial Multiagent environment. And then effectiveness of the proposed Naive Bayesian based policy model is analyzed through experiments using this game as test-bed.

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Game Bot Detection Approach Based on Behavior Analysis and Consideration of Various Play Styles

  • Chung, Yeounoh;Park, Chang-Yong;Kim, Noo-Ri;Cho, Hana;Yoon, Taebok;Lee, Hunjoo;Lee, Jee-Hyong
    • ETRI Journal
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    • v.35 no.6
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    • pp.1058-1067
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    • 2013
  • An approach for game bot detection in massively multiplayer online role-playing games (MMORPGs) based on the analysis of game playing behavior is proposed. Since MMORPGs are large-scale games, users can play in various ways. This variety in playing behavior makes it hard to detect game bots based on play behaviors. To cope with this problem, the proposed approach observes game playing behaviors of users and groups them by their behavioral similarities. Then, it develops a local bot detection model for each player group. Since the locally optimized models can more accurately detect game bots within each player group, the combination of those models brings about overall improvement. Behavioral features are selected and developed to accurately detect game bots with the low resolution data, considering common aspects of MMORPG playing. Through the experiment with the real data from a game currently in service, it is shown that the proposed local model approach yields more accurate results.

Stealthy Behavior Simulations Based on Cognitive Data (인지 데이터 기반의 스텔스 행동 시뮬레이션)

  • Choi, Taeyeong;Na, Hyeon-Suk
    • Journal of Korea Game Society
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    • v.16 no.2
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    • pp.27-40
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    • 2016
  • Predicting stealthy behaviors plays an important role in designing stealth games. It is, however, difficult to automate this task because human players interact with dynamic environments in real time. In this paper, we present a reinforcement learning (RL) method for simulating stealthy movements in dynamic environments, in which an integrated model of Q-learning with Artificial Neural Networks (ANN) is exploited as an action classifier. Experiment results show that our simulation agent responds sensitively to dynamic situations and thus is useful for game level designer to determine various parameters for game.

Motivation based Behavior Sequence Learning for an Autonomous Agent in Virtual Reality

  • Song, Wei;Cho, Kyung-Eun;Um, Ky-Hyun
    • Journal of Korea Multimedia Society
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    • v.12 no.12
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    • pp.1819-1826
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    • 2009
  • To enhance the automatic performance of existing predicting and planning algorithms that require a predefined probability of the states' transition, this paper proposes a multiple sequence generation system. When interacting with unknown environments, a virtual agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. We describe a sequential behavior generation method motivated from the change in the agent's state in order to help the virtual agent learn how to adapt to unknown environments. In a sequence learning process, the sensed states are grouped by a set of proposed motivation filters in order to reduce the learning computation of the large state space. In order to accomplish a goal with a high payoff, the learning agent makes a decision based on the observation of states' transitions. The proposed multiple sequence behaviors generation system increases the complexity and heightens the automatic planning of the virtual agent for interacting with the dynamic unknown environment. This model was tested in a virtual library to elucidate the process of the system.

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Utilization of Simulation and Machine Learning to Analyze and Predict Win Rates of the Characters Battle

  • Kang, Hyun-Syug
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.7
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    • pp.39-46
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    • 2020
  • Recently, for designing virtual characters in the battle game field effectively, some methods are very needed to predicate the win rates of the battle of them efficiently. In this paper, we propose a method to solve this problem by combining simulation and machine learning. Firstly, a simulation is used to analyze the win rates of the battle of virtual characters in the battle game. In addition, we apply a regression model based machine learning scheme to predict win rates of the battle of virtual characters according to their abilities. Our experimental results using suggested method show that it is almost no difference between the win rates of the simulation and the prediction results using the machine learning scheme. And also, we can obtain good performance in the experiment using only simple regression based machine learning model.

Why Should I Ban You! : X-FDS (Explainable FDS) Model Based on Online Game Payment Log (X-FDS : 게임 결제 로그 기반 XAI적용 이상 거래탐지 모델 연구)

  • Lee, Young Hun;Kim, Huy Kang
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.32 no.1
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    • pp.25-38
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    • 2022
  • With the diversification of payment methods and games, related financial accidents are causing serious problems for users and game companies. Recently, game companies have introduced an Fraud Detection System (FDS) for game payment systems to prevent financial incident. However, FDS is ineffective and cannot provide major evidence based on judgment results, as it requires constant change of detection patterns. In this paper, we analyze abnormal transactions among payment log data of real game companies to generate related features. One of the unsupervised learning models, Autoencoder, was used to build a model to detect abnormal transactions, which resulted in over 85% accuracy. Using X-FDS (Explainable FDS) with XAI-SHAP, we could understand that the variables with the highest explanation for anomaly detection were the amount of transaction, transaction medium, and the age of users. Based on X-FDS, we derive an improved detection model with an accuracy of 94% was finally derived by fine-tuning the importance of features that adversely affect the proposed model.

Repeated Overlapping Coalition Game Model for Mobile Crowd Sensing Mechanism

  • Kim, Sungwook
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.7
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    • pp.3413-3430
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    • 2017
  • With the fast increasing popularity of mobile services, ubiquitous mobile devices with enhanced sensing capabilities collect and share local information towards a common goal. The recent Mobile Crowd Sensing (MCS) paradigm enables a broad range of mobile applications and undoubtedly revolutionizes many sectors of our life. A critical challenge for the MCS paradigm is to induce mobile devices to be workers providing sensing services. In this study, we examine the problem of sensing task assignment to maximize the overall performance in MCS system while ensuring reciprocal advantages among mobile devices. Based on the overlapping coalition game model, we propose a novel workload determination scheme for each individual device. The proposed scheme can effectively decompose the complex optimization problem and obtains an effective solution using the interactive learning process. Finally, we have conducted extensive simulations, and the results demonstrate that the proposed scheme achieves a fair tradeoff solution between the MCS performance and the profit of individual devices.

Cognitive Radio Anti-Jamming Scheme for Security Provisioning IoT Communications

  • Kim, Sungwook
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.10
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    • pp.4177-4190
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    • 2015
  • Current research on Internet of Things (IoT) has primarily addressed the means to enhancing smart resource allocation, automatic network operation, and secure service provisioning. In particular, providing satisfactory security service in IoT systems is indispensable to its mission critical applications. However, limited resources prevent full security coverage at all times. Therefore, these limited resources must be deployed intelligently by considering differences in priorities of targets that require security coverage. In this study, we have developed a new application of Cognitive Radio (CR) technology for IoT systems and provide an appropriate security solution that will enable IoT to be more affordable and applicable than it is currently. To resolve the security-related resource allocation problem, game theory is a suitable and effective tool. Based on the Blotto game model, we propose a new strategic power allocation scheme to ensure secure CR communications. A simulation shows that our proposed scheme can effectively respond to current system conditions and perform more effectively than other existing schemes in dynamically changeable IoT environments.