• Electronics and Telecommunications Trends
• /
• v.39 no.3
• /
• pp.58-68
• /
• 2024

Quantum computing can be a game changer in all areas of economy security, and society, and it is expected to bring innovation to the entire industrial ecosystem. The competition in this industry is accelerating with various countries pushing for policies to preempt technology and maintain a technological advantage. The quantum computing market is expected to show commercial gains in 2027 as an inflection point. Initially, it is expected to replace the high-performance computing market. However the continuous growth of quantum computing will depend on the development innovative services such as ChatGPT in AI, and technological breakthroughs. We examine the definition and outlook, evolution and dynamics, and supply chain structure of the quantum computing industry in an attempt to unveil implications for its development.

• 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.

• Journal of Korea Game Society
• /
• v.21 no.6
• /
• pp.31-40
• /
• 2021

Go has a history of more than 2,500 years, and the life-and-death problems in Go is a fundamental problem domain that must be solved when implementing a computer Go. We attempted to determine the numbers of prototypical eye shapes with 3, 4, 5, and 6 eyes that are directly related to the life-and-death problems, and to classify the prototypical eye shapes represented in 4-tuple forms. Experiment was conducted by Monte-Carlo method and point pattern matching. According to the experimental results, the numbers of prototypical eye shapes were 2 for 3-eye, 5 for 4-eye, 12 for 5-eye, and 35 for 6-eye shapes. Further, using a 4-tuple form, we classified prototypical eye shapes into 1 for 3-eye, 3 for 4-eye, 4 for 5-eye, and 8 for 6-eye shapes.

• KIPS Transactions on Computer and Communication Systems
• /
• v.10 no.5
• /
• pp.155-162
• /
• 2021

n-step TD learning is a combination of Monte Carlo method and one-step TD learning. If appropriate n is selected, n-step TD learning is known as an algorithm that performs better than Monte Carlo method and 1-step TD learning, but it is difficult to select the best values of n. In order to solve the difficulty of selecting the values of n in n-step TD learning, in this paper, using the characteristic that overestimation of Q can improve the performance of initial learning and that all n-step returns have similar values for Q ≈ Q^*, we propose a new learning target, which is composed of the maximum and the mean of all k-step returns for 1 ≤ k ≤ n. Finally, in OpenAI Gym's Atari game environment, we compare the proposed algorithm with n-step TD learning and proved that the proposed algorithm is superior to n-step TD learning algorithm.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.1
• /
• pp.153-160
• /
• 2021

In recent universities, the new-normal era. There is a growing interest in nurturing creative thinking learners who have converged thinking necessary for the AI era based on big data. This study intends to propose the expansion of science liberal arts education necessary to meet the education needs of these universities. To this end, first the status of convergence education in universities and the elements necessary for the learners' creative ideas were examined. Second, learners who took a convergence lecture at a university will look at the development case of in which convergence thinking, creative thinking, and gaming are implemented. Based on this, we would like to propose an expanded organization and operation of science liberal arts education as a way to enhance the creative thinking of learners and their competencies through convergence classes at universities.

• The Journal of Society for e-Business Studies
• /
• v.25 no.2
• /
• pp.15-27
• /
• 2020

This study starts with the idea that the process of creating a Gantt Chart for schedule planning is similar to Tetris game with only a straight line. In Tetris games, the X axis is M machines and the Y axis is time. It is assumed that all types of orders can be worked without separation in all machines, but if the types of orders are different, setup cost will be incurred without delay. In this study, the game described above was named Gantris and the game environment was implemented. The AI-scheduling table through in-depth reinforcement learning compares the real-time scheduling table with the human-made game schedule. In the comparative study, the learning environment was studied in single order list learning environment and random order list learning environment. The two systems to be compared in this study are four machines (Machine)-two types of system (4M2T) and ten machines-six types of system (10M6T). As a performance indicator of the generated schedule, a weighted sum of setup cost, makespan and idle time in processing 100 orders were scheduled. As a result of the comparative study, in 4M2T system, regardless of the learning environment, the learned system generated schedule plan with better performance index than the experimenter. In the case of 10M6T system, the AI system generated a schedule of better performance indicators than the experimenter in a single learning environment, but showed a bad performance index than the experimenter in random learning environment. However, in comparing the number of job changes, the learning system showed better results than those of the 4M2T and 10M6T, showing excellent scheduling performance.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.879-884
• /
• 2004

This paper describes the development of a synthetic multi-agent called KMITL Cadence 2003. KMITL Cadence 2003 is a robotic soccer simulation team consisting of eleven autonomous software agents. Each agent operates in a physical soccer simulation model called Robocup Soccer Server which provides fully distributed and real-time multi-agent system environment. All teammates have to cooperate to achieve the common goal of winning the game. The simulation models many aspects of the football field such as noise in ball movements, noisy sensors, unreliable communication channel between teammates and actuators, limited physical abilities and restricted communication. This paper addresses the algorithm to develop the soccer agents to perform basic actions which are scoring, passing ball and blocking the opponents effectively. The result of this development is satisfactory because the successful scoring attempts is increased from 11.1% to 33.3%, successful passing ball attempts is increased from 22.08% to 63.64%, and also, successful intercepting attempts is increased from 88% to 97.73%.

• Journal of Korean Elementary Science Education
• /
• v.37 no.2
• /
• pp.126-146
• /
• 2018

The current study explores students' views about artificial intelligence (AI) through analyses of their drawings and perceptions. The data were gathered from a total of 177 elementary school students. The constant comparative analysis was used as the data analysis method. Based on the result, the current study found that students' views about artificial intelligence were constructed into two distinct dimensions: form and relationship. The form dimension, students' views about artificial intelligence were categorized into human, household goods, machine, smart computer, electronic chip/algorithm, or the hybridized form related to the game of go such as AlphaGo. On the relationship dimension, students' views about artificial intelligence were categorized into servants, friends or enemy. Given the combination of two dimensions, the current study found two noted patterns. The first, students who viewed artificial intelligence as human form perceived artificial intelligence as a friend or an enemy. However, those who viewed artificial intelligence as non-human form perceived artificial intelligence as a servant or an enemy. Based on these results, educational implications related to the preparation of artificial intelligence era for elementary science education are discussed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.05a
• /
• pp.317-321
• /
• 2006

게임의 재미요소를 증대 시키고, 게임 생명주기(Life-Cycle)를 늘어나게 하기 위해 다양한 방법이 연구 중이다. 현실감 있는 그래픽 효과와 뛰어난 음향 효과 등과 함께 게임 플레이어의 게임 스타일이 반영된 게임을 만들기 위한 방법이 대표적이 예라 할 수 있다. 그 중 게임 플레이어의 스타일을 게임에 다시 이용하기 위해서는 플레이어의 인지과정이 요구되며, 인지된 결과를 이용하여 플레이어를 모델링(User Modeling)한다. 하지만, 게임의 종류와 특성에 따라 다양한 게임이 존재하기 때문에 플레이어를 모델링하기 어렵다는 문제를 가지고 있다. 본 논문에서는 게임에서 정의된 FSM(Finite State machine)을 이용하여 플레이어가 선택한 행동 패턴을 분석하고 적용하는 방법과 다양한 게임에서 이용 할 수 있는 스크립트 형태의 NPC 행동 패턴 변경 방법을 제안한다. 플레이어의 데이터를 분석하여 얻은 결과는 FSM을 변경하여 새로운 행동을 보이는 NPC(Non-Player Characters)를 생성하는데 사용되며, 이 캐릭터는 게임의 특성과 플레이어의 최신 행동 패턴 경향을 학습한 적용형 NPC라 할 수 있다. 실험을 통하여 사용자의 행동과 유사한 패턴을 보이는 NPC의 생성을 확인할 수 있었으며, 게임에서 상대적인 또는 적대적인 캐릭터로 유용하게 사용 될 수 있다.

• Proceedings of the Korea Contents Association Conference
• /
• 2005.11a
• /
• pp.412-415
• /
• 2005

Recently, new games have been experimented to be given the adaptive interfaces equipped with AI algorithms which could interpret the user's personality, behavioral patterns, and physiological conditions in realtime. In this paper, we propose the intermediate form of tangible interface for PC package, which is currently decaying in commercial market.