• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.511-515
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• 2001

Many intelligent agent systems are known to incorporate BDI architecture for cognitive reasoning. Since this architecture contains all the knowledge of world model and reasoning rule, it is very complex and difficult to handle. This paper describes a methodology to design and implement BDI architecture, BDIAXml based on XML for multi-agent systems. This XML-based BDI architecture is smaller than any other BDI architecture because it separates knowledge for reasoning from domain knowledge and enables knowledge sharing using XML technology. Knowledge for BDI mental state and reasoning is composed of specific XML files and these XML files are stored into a specific knowledge server. Most systems using BDIAxml architecture can access knowledge from this server. We apply this BDIAXml system to domain of Hospital Information System and show that this architecture performs more efficiently than other BDI architecture system in terms of knowledge sharing, system size, and ease of use.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.91-103
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• 2004

Multi-agent systems for semantic web applications require efficient implementation of agent architectures without sacrificing the flexibility and the level of abstraction that agent architectures provide. In this paper, we present an agent system, called VivAce, which is implemented in Java to achieve both high efficiency and the level of abstraction provided by the BDI agent architecture. VivAce (Vivid Agent Computing Environment) has the characteristics of a vivid agent through the BDI agent model. A vivid agent is a software-controlled system whose state comprises the mental components of knowledge, perceptions, tasks, and intentions, and whose behavior is represented by means of action and reaction rules. We first identify the requirements for multi-agent systems and then present the relevant features of VivAce and experimental results.

• Korean Journal of Cognitive Science
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• v.11 no.1
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• pp.67-80
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• 2000

Distributed cooperative information system is the one that has various knowledge sources as well as problem solving capabilities to get information in a distributed and heterogeneous data environment. In a distributed cooperative information system. a control mechanism to facilitate the available information is very important. and usually the role of the control mechanism determines the behavior of the total system In this research. we proposed a model of the distributed cooperative information system which is based on the multi-agent paradigm. We also implemented a test system to show l its feasibility. The proposed system makes the knowledge sources into agents and a special agent called 'facilitator' controls the cooperation between the knowledge agents The facilitator uses the knowledge granularity level to determine the sequence of the activation of the agents. In other words. the knowledge source with simple but fast processing mechanism activates first while more sophisticated but slow knowledge sources are activated late. In an environment in which we have several knowledge sources for the same topic. the proposed system will simulate the focusing mechanism of human cognitive process.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3548-3558
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• 1999

There are many kinds of multi-agent frameworks which support the high-level knowledge representation languages for providing intelligence to their agents. But, the agent programming interfaces of the frameworks require to use some general-purpose programming languages as well as tile knowledge representation languages. In general, knowledge representation languages and general-purpose programming languages are different in their levels and data representation models. The differences can make the problems about tile coupling of the elements which are necessary for developing intelligent agents. This paper describes a new type of intelligent agent shell INAS(INtelligent Agent Shell) version 2 which has developed to cope with the problems. Unlike the previous agent frameworks, INAS supports a high-level integrated agent building language for building intelligent agents by itself. Therefore, the development of intelligent agents by using INAS version 2 does not suffer from the problems of the previous agent frameworks. Through the development of several intelligent agents, we experienced that the agent building language of INAS version 2 could reduce the difficulties of developing intelligent agents.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.427-432
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• 2012

The article herein suggested a compound repository and a descriptive method to develop a compound knowledge process. A data target saved in a compound knowledge repository suggested in this article includes all compound knowledge meta data and digital resources, which can be divided into the three following factors according to the purpose: user roles, functional elements, and service ranges. The three factors are basic components to describe abstract models of repository. In this article, meta data of compound knowledge are defined by being classified into the two factors. A component stands for the property about a main agent, activity unit or resource that use and create knowledge, and a context presents the context in which knowledge object are included. An agent of the compound knowledge process performs classification, registration, and pattern information management of composite knowledge, and serves as data flow and processing between compound knowledge repository and user. The agent of the compound knowledge process consists of the following functions: warning to inform data search and extraction, data collection and output for data exchange in an distributed environment, storage and registration for data, request and transmission to call for physical material wanted after search of meta data. In this article, the construction of a compound knowledge repository for recommendation system to be developed can serve a role to enhance learning productivity through real-time visualization of timely knowledge by presenting well-put various contents to users in the field of industry to occur work and learning at the same time.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.41-49
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• 1998

Agent, as a independent module, exchanges knowledge & information which are classified to their characteristics according to shared protocol. i.e. Agent Communication Language(AC1,). Fuzzy inference system represents the experiential knowledge as li~~guisticco ntrol rule and enables us to execute the knowledge using fuzzy inference. This study tries connecting fuzzy inference system with agent-based system and inspects applicability to hull form design through inferring principle dimension and hull form coefficients.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.439-444
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• 2011

As construction projects have been getting larger and more complex, a single individual or organization cannot have complete knowledge or the abilities to handle all matters. Collaborative practices among heterogeneous individuals, which are temporarily congregated to carry out a project, are required in order to accomplish project objectives. These organizational knowledge creation processes of project teams should be understood from the active and dynamic viewpoint of how they create information and knowledge rather than from the passive and static input-process-output sequence. To this end, agent-based modeling and simulation which is built from the ground-up perspective can provide the most appropriate way to systematically investigate them. In this paper, agent-based modeling and simulation as a research method and a medium for representing theory is introduced. To illustrate, an agent-based simulation of the evolution of collaboration in large-scale project teams from a game theory and social network perspective is presented.

• Knowledge Management Research
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• v.10 no.3
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• pp.17-33
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• 2009

The emergence of the Internet shopping agent enabled consumers to enjoy Internet shopping more easily and quickly. Especially, the role of Internet shopping agent is becoming more important following the information overload trend on the Internet in that the consumers can promptly obtain information about a certain product and its price among countless items on the Internet. As a result, consumers can now enjoy shopping more easily, compared to the offline shopping which requires a lot of efforts in comparing the products and purchasing them. Since the Internet shopping agents collect extensive information about the products' price, delivery period, detailed characteristics, etc., and present a comparison table containing the information to the consumers, the consumers can shop more quickly at lower price using such shopping agents. However, it has not been sufficiently studied about how the various functions of shopping agents actually support consumers' purchase decision making procedure in everyday life, and if they do, in which stages they play a supporting role in consumers' purchase decision making system. Therefore, this study conducts an empirical analysis on the role of the Internet shopping agents in the purchase decision making process of the consumers, considering the Internet shopping agent as a decision making supporting system. Moreover, it analyzes how the effects of the Internet shopping agent differs according to the consumers' knowledge level about the products.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.257-261
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• 2003

In this study, we investigated the modeling and design of an Intelligent Agent System (IAS). To achieve this goal, we introduced several kinds of agents that exhibit intelligent features. These are the main agent, management agent, watcher agent, report agent and application agent. We applied the intelligent agent concept to two different application fields, i.e. the intelligent agent system for pattern classification and the intelligent agent system for bank asset management modeling.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.1
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• pp.173-182
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• 2001

In this paper, we discuss how ontology Plays roles in building a distributed and heterogeneous knowledge-base system. First, we discuss relationship between ontology and agent in the Knowledgeable Community which is a framework of knowledge sharing and reuse based on a multi-agent architecture. Ontology is a minimum requirement for each agent to join the Knowledgeable Community. Second we explain mediation by ontology to show how ontology is used in the Knowledgeable Community. A special agent called mediation analyzes undirected messages and infer candidates of recipient agents by consulting ontology and relationship between ontology and agents. Third we model ontology as combination of aspects each of which can represent a way of conceptualization. Aspects are combined either as combination aspect which means integration of aspects or category aspect which means choice of aspects. Since ontology by aspect allows heterogeneous and multiple descriptions for phenomenon in the world, it is appropriate for heterogeneous knowledge-base systems. We also show translation of messages as a wave of interpreting multiple aspects. A translation agent can translate a message with some aspect to one with another aspect by analyzing dependency of aspects. Mediation and translation of messages are important to build agents easily and naturally because less knowledge on other agents is requested for each agent.