• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.3001-3010
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• 1999

The most of intrusion detection methods do not detect intrusion on real-time because it takes a long time to analyze an auditing data for intrusions. To solve the problem, we are studying a real-time intrusion detection. Therefore, this paper proposes an agent model using multi warning level for real-time intrusion detection. It applies to distributed environment using an extensibility and communication mechanism among agents, supports a portability, an extensibility and a confidentiality of IDS.

• Journal of Information Processing Systems
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• v.6 no.3
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• pp.385-402
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• 2010

Most of the data warehouse (DW) requirements engineering approaches have not distinguished the early requirements engineering phase from the late requirements engineering phase. There are very few approaches seen in the literature that explicitly model the early & late requirements for a DW. In this paper, we propose an AGDI (Agent-Goal-Decision-Information) model to support the early and late requirements for the development of DWs. Here, the notion of agent refers to the stakeholders of the organization and the dependency among agents refers to the dependencies among stakeholders for fulfilling their organizational goals. The proposed AGDI model also supports three interrelated modeling activities namely, organization modeling, decision modeling and information modeling. Here, early requirements are modeled by performing organization modeling and decision modeling activities, whereas late requirements are modeled by performing information modeling activities. The proposed approach has been illustrated to capture the early and late requirements for the development of a university data warehouse exemplifying our model's ability of supporting its decisional goals by providing decisional information.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.45-55
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• 2022

Recently, large-scale language models (LPLM) have been shown state-of-the-art performances in various tasks of natural language processing including intent classification. However, fine-tuning LPLM requires much computational cost for training and inference which is not appropriate for dialog system. In this paper, we propose compressed intent classification model for multi-agent in low-resource like CPU. Our method consists of two stages. First, we trained sentence encoder from LPLM then compressed it through knowledge distillation. Second, we trained agent-specific adapter for intent classification. The results of three intent classification datasets show that our method achieved 98% of the accuracy of LPLM with only 21% size of it.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.33-40
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• 2009

We study Interpreted Systems, ATL, and ATEL to capture the notion of time, knowledge, and strategy which are important in the analysis of multi-agent systems and propose strategic constraints based on subgame perfect Nash equilibrium of game theory as one of the solutions for the issues of ATEL which an agent can access the current state of the whole system when making up his strategy even when he should be uncertain about the state, and no explicit representation of actions in ATEL models makes some natural situations harder to model. Also, we present strategic constraints-based Interpreted Systems for model checking of multi-agent systems.

• Journal of Korean Society for Quality Management
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• v.42 no.2
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• pp.221-234
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• 2014

Purpose: The Purpose of this study is to develop a model for predicting agent churn group in the cosmetics industry. We develope two models, pattern model and matrix model, which are compared regarding the prediction accuracy of churn agents. Finally, we try to conclude if there is statistically significant difference between two models by empirical study. Methods: We develop two models using the part of RFM(Recency, Frequency, Monetary) method which is one of customer segmentation method in traditional CRM study. In order to ensure which model can predict churn agents more precisely between two models, we used CRM data of cosmetics company A in China. Results: Pattern model and matrix model have been developed. we find out that there is statistically significant differences between two models regarding the prediction accuracy. Conclusion: Pattern model and matrix model predict churn agents. Although pattern model employed the trend of monetary mount for six months, matrix model that used the amount of sales per month and the duration of the employment is better than pattern model in prediction accuracy.

• The Journal of Korean Association of Computer Education
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• v.10 no.5
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• pp.43-51
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• 2007

We have developed a peer agent to support programming learning and analyzed its educational effects in a programming course. The agent acts as a tutor or a tutee. The role of a tutor/tutee is like the role of a navigator/driver in pair programming. While students are learning with the peer agent, the students' programming abilities are modeled. Based on the student's model, the peer agent provides appropriate feedbacks and contents to the learner. The peer agent gives positive effects on learners' achievement and self-efficacy in a programming course. It means that the peer agent system helps the learner in an affective domain as well as a cognitive domain.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.209-211
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• 2000

This paper presents a Multi-Agent system for setting and coordination of overcurrent protective devices in the distribution system. The simulation model shows its feasibility in which KQML is used for communication among agents. The proposed system could make it possible to keep the protection system in the optimal state all the time.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.149.5-149
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent´s dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless ...

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.277-282
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• 2004

This paper outlines our approach and the underlying design principles aimed at the generation of a theory of coordination. Such theory would assist in designing new Multi-Agent Systems(MAS) and provide trouble-shooting tools for suboptimally functioning MAS. This paper also describes the decisions that have been made in this endeavor. We have been able to show via a simplified model that approach is feasible and can produce results.

• Korean Journal of Cognitive Science
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• v.10 no.1
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• pp.1-11
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• 1999

Agent-based systems technology has generated lots of excitement in these years because of its promise as a new paradigm for conceptualizing. designing. and l implementing software systems Especially, there has been many researches for multi agent system because of the characteristics that it fits to the distributed and open Internet environments. In a multiagent system. agents must cooperate with each other through a Coordination procedure. when the conflicts between agents arise. where those are caused b by the point that each action acts for a purpose separately without coordination. But P previous researches for coordination methods in multi agent system have a deficiency that they can not solve correctly the cooperation problem between agents which have different goals in dynamic environment. In this paper. we solve the cooperation problem of multiagent that has multiple goals in a dynamic environment. with an automatic cooperative coordination model using I reinforcement learning. We will show the two pursuit problems that we extend a traditional problem in multi agent systems area for modeling the restriction in the multiple goals in a dynamic environment. and we have verified the validity of the proposed model with an experiment.