• Journal of Korea Game Society
• /
• v.20 no.2
• /
• pp.113-122
• /
• 2020

This study is the research tendency(2016~2019) on the content and the intelligent information technology. After the IIT emerged as a social topic, related research increased, and interest in VR and AR was the highest. In games, more research has been done on VR and AR. In the case of big data technology, it was a tendency to pay attention to the study of movie contents. Many studies have attempted a technological approach to IIT. With regard to artificial intelligence technology, there were differences by technology and content area, mainly viewed from a legal and institutional perspective.

• Journal of KIISE
• /
• v.45 no.1
• /
• pp.30-35
• /
• 2018

GuessWhat?! is a game in which two machine players, composed of questioner and answerer, ask and answer yes-no-N/A questions about the object hidden for the answerer in the image, and the questioner chooses the correct object. GuessWhat?! has received much attention in the field of deep learning and artificial intelligence as a testbed for cutting-edge research on the interplay of computer vision and dialogue systems. In this study, we discuss the objective function and characteristics of the GuessWhat?! game. In addition, we propose a simple solver for GuessWhat?! using a simple rule-based algorithm. Although a human needs four or five questions on average to solve this problem, the proposed method outperforms state-of-the-art deep learning methods using only two questions, and exceeds human performance using five questions.

• Proceedings of the Korea Contents Association Conference
• /
• 2010.05a
• /
• pp.49-51
• /
• 2010

스마트폰의 도입으로 인하여 증강현실이 널리 알려짐에 따라 대중들의 관심은 이에 집중되고 있으며 휴대성으로 인하여 모바일기기에서의 증강현실 연구가 하나의 흐름으로 자리 잡고 있다. 기존의 증강현실과 인공지능이 결합된 연구들은 주로 로봇공학이 많은 비율을 차지하고 있으며 게임에 접목된 연구들은 부족한 실정이다. 또한 인공지능이 적용된 에이전트들의 움직임을 위한 데이터들은 아직까지 사용자가 직접 입력해주거나 이를 인식하는데 마커를 사용하고 있다. 본 논문에서는 마커리스 추적 기술을 사용하여 생성한 데이터를 인공지능부분에서 사용하며 증강현실 기반의 인공지능이 적용된 에이전트를 위한 게임 시스템을 제안한다. 그리고 이를 아이폰 모바일 기기에서 구현하였으며 인식율, 정확도를 측정하여 본 시스템을 검증하였다.

• Journal of the Semiconductor & Display Technology
• /
• v.21 no.3
• /
• pp.110-113
• /
• 2022

Non-playable game character plays an important role in improving the concentration of the game and the interest of the user, and recently implementation of NPC with reinforcement learning has been in the spotlight. In this paper, we estimate an AI target tracking method via reinforcement learning, and implement an AI-based tracking agency of specific target object with avoiding traps through Unity ML-Agents Toolkit. The implementation is built in Unity game engine, and simulations are conducted through a number of experiments. The experimental results show that outstanding performance of the tracking target with avoiding traps is shown with good enough results.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.432-439
• /
• 2022

This paper introduces deliberative behavior tree using utility theory. The proposed approach combine the strengths of behavior trees and utility theory to implement complex behavior of AI agents in an easier and more concise way. To achieve this goal, we devised and implemented three types of additional behavior tree nodes, which evaluate utility values of its own node or its subtree while traversing and selecting its child nodes based on the evaluated values. In order to validate our approach, we implemented a sample scenario using conventional behavior tree and our proposed deliberative tree respectively. And then we compared and analyzed the simulation results.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.07a
• /
• pp.277-278
• /
• 2023

본 논문은 머신러닝 학습 도구인 Teachable Machine을 활용하여 모션 인식 러닝 액션 게임인 Dino Run Game을 개발하는 것을 목표로 한다. JavaScript, HTML, CSS를 사용하여 기본적인 게임 프레임워크를 구현하고, Google에서 개발한 Teachable Machine의 이미지 인식 모델을 활용하여 웹캠을 통해 사용자의 손 이미지를 인식한다. 이를 기반으로 게임 캐릭터를 제어함으로써 키보드를 사용하지 않고도 게임을 즐길 수 있도록 구현한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.12
• /
• pp.4625-4647
• /
• 2020

The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of "generative" and "adversarial", researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

• Journal of Korea Game Society
• /
• v.13 no.6
• /
• pp.43-52
• /
• 2013

Path finder is one of the very important algorithm of artificial intelligence and is a process generally used in many game fields. Path finder requires many calculation, so it exerts enormous influences on performances. To solve this, many researches on the ways to reduce the amount of calculate operations have been made, and the typical example is A* algorithm but it has unnecessary computing process, reducing efficiency. In this paper, to reduce the amount of calculate operations such as node search with costly arithmetic operations, we proposes the weight based pre-processing A* algorithm. The simulation was materialized to measure the efficiency of the weight based pre-process A* algorithm, and the results of the experiments showed that the weight based method was approximately 1~2 times more efficient than the general methods.

• Journal of Korea Game Society
• /
• v.20 no.3
• /
• pp.3-14
• /
• 2020

The purpose of this paper is to analyze the factors effecting users' satisfaction on game AI for early AI diffusion. For this purpose, we interviewed 20 users who had experiences playing Blade&Soul, made by NCsoft. Interview data had been analyzed through the Semantic Network Analysis program to identify key subject words and their relations. As a result, the paper has found keywords such as patterns, contents, variety, system, and getting new users as factors effecting users satisfaction on game AI.

• Journal of Science and Technology Studies
• /
• v.17 no.1
• /
• pp.5-39
• /
• 2017

In March 2016, the computer Go program, AlphaGo, defeated Sedol Lee, a Korean professional Go player of 9-dan rank. This victory by AlphaGo shows the rise in popularity of artificial intelligence (AI). Not only was this game a testament to machine performance, it was the type of game that extended the Turing test. When the interrogator cannot differentiate between human being and machine, the machine has passed the test. This article examines the interactions between AI and human beings and studies the social nature of intelligence through the AlphaGo case. Collins insists that knowledge or intelligence is social and embodied, and the interrogators in the Turing test can identify the difference between native members and non-members through their knowledge only. Applying this concept, AlphaGo, as subject A of this test, fulfilled its role of stirring up the classical "truth of human." Meanwhile, Lee as subject B, played to speak the truth by revealing his own qualities. Here, it is also important role that interrogators judge what it is. Many spectators, as interrogators, have intervened to confirm the border between human beings and machines by using their embodied and social knowledge.