• The Journal of Korean Association of Computer Education
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• v.23 no.4
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• pp.1-11
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• 2020

The purpose of this study is to propose the direction of AI education in K-12 education through investigating and analyzing aspects of the purpose, content, and methods of AI education as the curriculum and teacher training factors. We collected and analyzed 9 papers as the primary literature and 11 domestic and foreign policy reports as the secondary literature. The collected literatures were analyzed by applying a descriptive reviews, and the implications were derived by analyzing the curriculum components and TPACK elements for multi-dimensional analysis. As a result of this study, AI education targets were divided into three steps: AI users, utilizer, and developers. In K-12 education, the user and utilizer stages are appropriate, and artificial intelligence literacy must be included for user education. Based on the current computing thinking ability and coding ability for utilizer education, the implication was derived that it is necessary to target the ability to create creative output by applying the functions of artificial intelligence. In addition to the pedagogical knowledge and the ability to use the platform, The teacher training is necessary because teachers need content knowledge such as problem-solving, reasoning, learning, perception, and some applied mathematics, cognitive / psychological / ethical of AI.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.139-155
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• 2016

The purpose of this study is to find the research identity of the Korea Intelligent Information Systems Society through the profiling methods and co-word analysis in the most recent three-year('2014~'2016) study to collect keyword. In order to understand the research identity for intelligence information system, we need that the relative position of the study will be to compare identity by collecting keyword and research methodology of The korea Society of Management Information Systems and Korea Association of Information Systems, as well as Korea Intelligent Information Systems Society for the similar. Also, Korea Intelligent Information Systems Society is focusing on the four research areas such as artificial intelligence/data mining, Intelligent Internet, knowledge management and optimization techniques. So, we analyze research trends with a representative journals for the focusing on the four research areas. A journal of the data-related will be investigated with the keyword and research methodology in Korean Society for Big Data Service and the Korean Journal of Big Data. Through this research, we will find to research trends with research keyword in recent years and compare against the study methodology and analysis tools. Finally, it is possible to know the position and orientation of the current research trends in Korea Intelligent Information Systems Society. As a result, this study revealed a study area that Korea Intelligent Information Systems Society only be pursued through a unique reveal its legitimacy and identity. So, this research can suggest future research areas to intelligent information systems specifically. Furthermore, we will predict convergence possibility of the similar research areas and Korea Intelligent Information Systems Society in overall ecosystem perspectives.

• Korea Information Processing Society Review
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• v.11 no.2
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• pp.76-87
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• 2004

기계번역은 자연어 처리 및 인공지능 분야에서 가장 어려 운 태스크 중의 하나로 인식되어 왔다. 이는 정확한 번역이란 텍스트에 대한 이해 없이는 불가능하기 때문이다. 그러한 이유로 연구자들은 한때 기계번역에 대한 부정적인 결론에 도달하기도 하였다. 지금까지 기계번역을 위해 다양한 방법이 연구되어 왔으며 이들 연구에서는 주로 두 언어에 대한어휘나 구의 대역사전, 숙어사전, 개별 언어의 문법, 혹은 변환규칙 및 변환사전, 문장생성에 관련된 지식, 의미나 실세계 지식, 도메인에 적합한 지식 등 번역의 방식과 목적에 따라 다양한 형태의 지식과 알고리즘이 적용되었으며 그 대부분은 방대한 양의 수작업에 의존적이었다.(중략)

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.347-350
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• 2019

인공지능 관련 기술의 발달로 다양한 분야에서 인공지능 활용에 대한 관심이 고조되고 있으며 전문영역에서도 기계학습 기법을 활용한 연구들이 활발하게 이루어지고 있다. 특허청에서는 분야별 전문지식을 가진 분류담당자가 출원되는 모든 특허에 국제특허분류코드(이하 IPC) 부여 작업을 수행하고 있다. IPC 분류와 같은 전문적인 업무영역에서 딥러닝을 활용한 자동 IPC 분류 서비스를 제공하기 위해서는 기계학습을 이용하는 분류 모델에 분야별 전문지식을 직관적으로 반영하는 것이 필요하다. 이를 위해 본 연구에서는 딥러닝 기반의 IPC 분류 모델과 전문지식이 반영된 분류별 어휘사전을 활용한 규칙기반 분류 모델을 병행하여 특허문서의 IPC분류를 자동으로 추천하는 방법을 제안한다.

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.

• KIISE Transactions on Computing Practices
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• v.21 no.8
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• pp.572-577
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• 2015

Generally, the existing Artificial Intelligence (AI) systems were designed for specific purposes and their capabilities handle only specific problems. Alternatively, Artificial General Intelligence can solve new problems as well as those that are already known. Recently, General Video Game Playing the game AI version of General Artificial Intelligence, has garnered a large amount of interest among Game Artificial Intelligence communities. Although video games are the sole concern, the design of a single AI that is capable of playing various video games is not an easy process. In this paper, we propose a GreedyUCB1 algorithm and rollout method that were formulated using the knowledge from a game analysis for the Monte-Carlo Tree Search game AI. An AI that used our method was ranked fourth at the GVG-AI (General Video Game-Artificial Intelligence) competition of the IEEE international conference of CIG (Computational Intelligence in Games) 2014.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.528-530
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• 1999

컴퓨터가 인식할 수 있는 지식의 표현방식에 대한 연구는 오래전부터 인공지능 분야에서 이루어져 왔다. 현재는 대규모의 지식베이스를 구축하고 공유하기 위한 Knowledge Sharing Effort(KSE) 프로젝트가 진행중인데, 여기에서는 도메인별로 어휘와 개념을 체계적으로 정의하여 Ontology로 구축하고 있다. 이 논문에서는 객체지향 소프트웨어 개발 방법론에서 모델링언어로서 널리 받아들여지고 있는 UML을, KSE에서 표준으로 제시하고 있는 컴퓨터가 인식할 수 있는 지식의 형태인 Knowledge Interchange Format 형태로 변환하는 방법을 제시하고 이를 통해 추론과 같은 보다 유연한 지식의 처리가 가능함을 보인다.

• Korean Management Science Review
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• v.3
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• pp.3-8
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• 1986

인공지능 기법을 응용하는 한 분야로서 전문가시스템에 대한 일반적인 개념을 소개하였다. 응용분야에서 전문가의 의사결정 수준과 버금가는 능력을 부여할 수 있는 시스템의 특성을 고려하여 전문가들의 지식을 시스템에 묘사하는 방법과 이를 이용하는 방법에 관하여 논하였다. 또한 지식획득 과정의 특수성을 고려하여 전문가시스템의 개발방식에 관하여도 논하였다.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.41-58
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• 1998

본 연구는 여러 가지 Data Mining 기법들로부터 도출된 지식과 AHP를 이용하여 도출된 전문가의 지식을 사용된 정보의 특성에 따라 조사하고, 이러한 각각의 지식들을 중심으로 부도예측 모형을 설계한 후, 각 모형의 특성 및 부도예측력에 대한 실증적 비교연구에 그 목적을 두고 있다. 사용된 Data Mining 기법들은 통계적 다중판별분석 모형, ID3 모형, 인공신경망 모형이며, 전문가 지식의 추출은 AHP를 사용하여 45명의 전문가로부터 부도와 관련하여 인터뷰 및 설문조사를 실시하였다. 특히 부도예측에 사용된 변수의 특성을 정량적 재무정보와 정성적 비재무정보로 나누어서 각 모형의 특성을 비교연구하였다. 연구결과 부도예측시 정성적정보의 중요성을 확인하였으며, 전문가의 지식을 기반으로한 AHP 모형이 위험예측모형으로 사용될 수 있음을 실증적으로 보여주었다.