• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.636-639
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• 2016

최근 모바일 게임에서의 인공지능과 관련된 연구가 활발히 진행되고 있다. 본 논문에서는 모바일 축구 시뮬레이션 게임에서 활용할 수 있는 인공지능 에이전트를 Hierarchical FSM 기반으로 설계하고 구현하여 실제 축구경기 결과와 비슷한 결과 도출하였다. 이러한 Hierarchical FSM을 기반으로 한 지능형 에이전트는 코드의 재활용성이 높고 개념적으로 간단하여 인공지능 에이전트를 설계 및 구현하기에 적합하다.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.225-230
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• 2022

The intelligent home network system integrates various devices in the home into one communication network to provide information sharing, control, and operation environment between devices. This intelligent home network system operates around a home server. Home appliances in the era of the 4th industrial revolution will have numerous home servers in logical areas as the intelligent home network in the home accelerates. Therefore, the need for systematic management of home servers is emerging. We propose an agent system for efficient intelligent smart home server management. The agent system monitors the home server and operating environment for home server management of the intelligent smart home network. By referring to this monitored information, the service module of the home server is managed, and the home server is dealt with whether it is normal or not. In addition, by referring to the information collected by the service agent created in the group management server while migrating the home server, it is possible to deal with integrated meter reading, crime prevention, and topics. And when a new service is applied to the home server, it is registered in the management server and distributed to the home server through the agent, so that the intelligent smart home network can be efficiently managed.

• Knowledge Management Research
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• v.21 no.1
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• pp.175-195
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• 2020

The development of platform service based on the information and communication technology has revolutionized patterns of commercial transactions, driving the growth of global economy. Furthermore, the radical advancement of artificial intelligence(AI) presents the huge potential to innovate almost all the industrial and economic activities. Given these technological developments, the goal of this paper is to investigate AI's impact on the platform service innovation as well as its influence on the business performance. For the goal, this paper presents the review of the types of service innovation, the nature of platform services, and technological characteristics of leading AI technologies, such as chatbot and recommendation system. As an empirical study, this paper performs a multiple case study of Kakao Group which is the leading mobile platform service with the most advanced AI in Korea. To understand the role and effect of AI on Kakao platform service, this study investigated three cases, including chatbot agent of Kakao Bank, Smart Call service of Kakao Taxi, and music recommendation system of Kakao Mellon. The analysis results of the case study show that AI initiated innovations in platform service concepts, service delivery, and customer interface, all of which lead to a significant decrease in the transaction costs and the personalization of services. Finally, for the successful development of AI, this research emphasizes the significance of the accumulation of customer and operational data, the AI human capital, and the design of R&D organization.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.399-406
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• 2023

Cloud computing has been evolved to support edge computing architecture that combines fog management layer with edge servers. The main reason why it is received much attention is low communication latency for real-time IoT applications. At the same time, various cloud task scheduling techniques based on artificial intelligence have been proposed. Artificial intelligence-based cloud task scheduling techniques show better performance in comparison to existing methods, but it has relatively high scheduling time. In this paper, we propose a deep learning-based dynamic scheduling with multi-agents supporting scalability in edge computing environments. The proposed method shows low scheduling time than previous artificial intelligence-based scheduling techniques. To show the effectiveness of the proposed method, we compare the performance between previous and proposed methods in a scalable experimental environment. The results show that our method supports real-time IoT applications with low scheduling time, and shows better performance in terms of the number of completed cloud tasks in a scalable experimental environment.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2246-2255
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• 2023

Evacuation time estimation (ETE) is crucial for the effective implementation of resident protection measures as well as planning, owing to its applicability to nuclear emergencies. However, as confirmed in the Fukushima case, the ETE performed by nuclear operators does not reflect behavioral features, exposing thus, gaps that are likely to appear in real-world situations. Existing research methods including surveys and interviews have limitations in extracting highly feasible behavioral features. To overcome these limitations, we propose a VR-based immersive experiment system. The VR system realistically simulates nuclear emergencies by structuring existing disasters and human decision processes in response to the disasters. Evacuation behavioral features were quantitatively extracted through the proposed experiment system, and this system was systematically verified by statistical analysis and a comparative study of experimental results based on previous research. In addition, as part of future work, an application method that can simulate multi-level evacuation dynamics was proposed. The proposed experiment system is significant in presenting an innovative methodology for quantitatively extracting human behavioral features that have not been comprehensively studied in evacuation. It is expected that more realistic evacuation behavioral features can be collected through additional experiments and studies of various evacuation factors in the future.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.879-884
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• 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%.

• Korean Journal of Cognitive Science
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• v.15 no.3
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• pp.35-44
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• 2004

As the use of internet becomes proliferated, a great amount of information is provided through diverse channels. Users require effective information providing service and we have studied the conversational agent that exchanges information between users and agents using natural language dialogue. In this paper, we develop a question answering agent providing the corresponding answer by analyzing the user's intention using artificial intelligence techniques such as pattern matching and Bayesian network We work out various problems in knowledge representation of users by constructing keyword synonym database. The proposed method is applied to designing an agent for the introduction of a fashion web site, which confirms that it responds more flexibly to the user's queries.

• Knowledge Management Research
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• v.20 no.4
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• pp.99-118
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• 2019

Online financial counseling services are increasingly expanding with the rise of artificial intelligence-based chatbots. It is very important to examine the effects of emoticons noted as alternatives for communicating emotions in online communication between consumers and companies. In this paper, we examine how the use of emoticons affects the consumer's response and investigate the moderating roles of type of counseling agents (human vs. chatbot) and the consumer's subjective financial knowledge. The results show that the use of emoticon in the conversation brings a positive effect on the consumer's intention to use of online chat counseling service. When participants had relatively low subjective financial knowledge, they had higher intention to use online chat counseling services with emoticons only when the agent type was chatbot. When the type of counseling agent was human, this positive effect of the emoticon did not occur. On the other hand, when participants had relatively high subjective financial knowledge, they had higher intention to use online chat counseling service with emoticons only when the agent type was human. This study contributes to providing practical implications to build online chat counseling service using chatbot in the financial industry by studying users' intention depending on the type of agents and the level of their subjective knowledge.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.919-936
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• 2003

Nowadays Electronic Commerce shopping mall is welcomed more and more on the Internet. It is expected that Shopping mall systems come to be various and adaptable to complex requirements according to customers who have these various needs, but just show products list, instead. This thesis suggests various structures of shopping malls showing interface agent model using Case-Based Reasoning one of reasoning method of Artificial Intelligence instead of the method of prior EC shopping mall. 1 constructed case base by making index with shopping mall members and customers' private informations, and pursued difference from prior EC shopping malls by proposing to customers cases of other users' selection of products who have similar private informations with them.