• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.45-54
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• 2009

The virtual world is simulated in terms of agents and the environment as the real world. Agents are able to perceive and recognize objects as well as components of the environmental elements in the virtual world. As a result, they are able to autonomously act in terms of their decision. They generate diverse situation that make the user to feel familiarity as they would be have like human beings. Such situations are constructed in terms of the concepts of the entities, relations and activities, and agents accordingly recognize the situations in terms of these concepts. In this paper, we propose the method to recognize and represent the shapes which are the most important feature among objects. Various shapes are represented in terms of primitive shapes and their spatial relations in depth. We use distance, direction, and orientation in order to represent basic spatial relation. In order to simplify basic signal processing, objects dynamically send messages of self information to agents, and refer to ontology as the knowledge base of the virtual world. Agents perceive the information from objects by comparing with schematic knowledge and instance knowledge in ontology.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.317-331
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• 2004

Recently software agent has been studied as a new abstraction unit of software engineering. The agent with autonomous, adaptability and cooperation attribute is accepted as a new abstraction unit especially in distributed systems, open systems, and complex systems. However, the progress of agent research has been slow and the realization of agent programming language seems to be far distant. Because the properties of agent are diverse, the opinions of researchers can not converge to one. In this situation, software agent framework is accepted more realistic alternative solution. However the knowledge for its development doesn't have been shared among developers. So they often have to make same errors. We will help sharing of knowledge and experience by using pattern language which has been used in object technology for long times. This paper proposes a reactive agent framework pattern language and validates it based on ATAM［l］ The increase of such indirect experience can reduce the waste of resource by preventing the same try and error. So agent framework developers are able to concentrate on more essential issues. Finally quality of software agent framework will be increased.

• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.49-55
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• 2013

Recently the CGF/SAF (Computer Generated Force / Semi-Automated Force) technology has been getting attention to deal with the increasing complexity in a DM&S(Defence Modeling and Simulation) environment. OneSAF is one of well-known CGF/SAF systems, however, it is not able to support the DEVS framework which is an advanced discrete event based modeling and simulation environment. Especially, most DM&S systems in Korea has been developed on the basis of the DEVS framework. In this paper, we have proposed the agent-based SAF design methodology and tool for supporting DEVS M&S environment. The proposed SAF modeling tool is divided into two parts; the agent modeling part and SAF modeling part. In the agent modeling environment, the modeler can simply create the agent model by writing down the necessary rules. It also provides the agent testing environment so that the modeler maybe conveniently verify the prescribed agent model. The SAF model is finally created by combing the individual agents based on the pre-defined structure. DM&S engineers will be able to employ our tools and modeling methodology to design the DEVS-based DM&S system to be developed.

• The Journal of Engineering Research
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• v.6 no.2
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• pp.11-22
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• 2004

A distributed constraint satisfaction problem (distributed CSP) is a constraint satisfaction problem(CSP) in which variables and constraints are distributed among multiple automated agents. ACSP is a problem to find a consistent assignment of values to variables. Even though the definition of a CSP is very simple, a surprisingly wide variety of AI problems can be formalized as CSPs. Similarly, various application problems in DAI (Distributed AI) that are concerned with finding a consistent combination of agent actions can be formalized as distributed CAPs. In recent years, many new backtracking algorithms for solving distributed CSPs have been proposed. But most of all, they have common drawbacks that the algorithm assumes the priority of agents is static. In this thesis, we establish a basic algorithm for solving distributed CSPs called dynamic priority search algorithm that is more efficient than common backtracking algorithms in which the priority order is static. In this algorithm, agents act asynchronously and concurrently based on their local knowledge without any global control, and have a flexible organization, in which the hierarchical order is changed dynamically, while the completeness of the algorithm is guaranteed. And we showed that the dynamic priority search algorithm can solve various problems, such as the distributed 200-queens problem, the distributed graph-coloring problem that common backtracking algorithm fails to solve within a reasonable amount of time. The experimental results on example problems show that this algorithm is by far more efficient than the backtracking algorithm, in which the priority order is static. The priority order represents a hierarchy of agent authority, i.e., the priority of decision-making. Therefore, these results imply that a flexible agent organization, in which the hierarchical order is changed dynamically, actually performs better than an organization in which the hierarchical order is static and rigid. Furthermore, we describe that the agent can be available to hold multiple variables in the searching scheme.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.351-368
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• 2005

The manual construction of the knowledge-base takes much time and effort, and it is hard to adjust intelligence systems to dynamic and flexible environment. Thus mental development in those systems has been investigated in recent years. Autonomous mental development is a new paradigm for developing autonomous machines, which are adaptive and flexible to the environment. Learning conversation, a kind of mental development, is an important aspect of conversational agents. In this paper, we propose a learning conversation method for conversational agents which uses several promising techniques; speech-act templates and genetic programming. Knowledge acquisition of conversational agents is implemented by finite state machines and templates, and dynamic sentence generation is implemented by genetic programming Several illustrations and usability tests how the usefulness of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.42-48
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• 2011

Agent acts for specification purpose, which is common element of CGF (Computer Generated Forces). When basic agent acts as planned, the advanced intelligence agent can do more than this. It can follow predefined actions along appointed script to achieve purpose or lay another plans when it is difficult to achieve. In other words, it can amend plan again or make new plan in order to achieve goals. When plan fails, agent amends oneself, possibly decreases target level to achieve easily. In doing so, the agent calculates a quantitative value for changing plans in realtime, and choose appropriate alternative plans when the threshold value reaches an limit. In this paper, we propose an military system in which the planned action can be modified according to the level of achievement and alternative plans can be generated accordingly.

• Journal of Internet Computing and Services
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• v.5 no.2
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• pp.97-105
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• 2004

The reason that an intelligent agent cannot understand the representation of its own perception or activity is caused by the traditional syntactic approach that translates a semantic feature into a simulated string, To implement an autonomously learning intelligent agent, Cohen introduces a experimentally semantic approach that the system learns a contentful representation of physical schema from physically interacting with environment using its own sensors and effectors. We propose that negation is a meta-level schema that enables an agent to recognize its own physical schema, To improve the planner's efficiency, Graphplan introduces the control rule that manipulates the inconsistency between planning operators, but it cannot cognitively understand negation and suffers from redundancy problem. By introducing a negative function not, IPP solves the problem, but its approach is still syntactic and is inefficient in terms of time and space. In this paper, we propose that, to represent a negative fact, a positive atom, which is called opposite concept, is a very efficient technique for implementing an cognitive agent, and demonstrate some empirical results supporting the hypothesis.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06a
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• pp.77-79
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• 2012

MSN 라우팅 프로토콜 설계에서 노드의 이동성 정도를 반영하는 것은 중요한 문제이며 다른 네트워크 인자의 특성들과 통합적으로 고려되어야 한다. 하지만, 기존 라우팅 기법들은 이동성의 고려가 미미하거나 다른 환경 인자들과 통합적인 고려가 되지 않았다. 본 논문에서는 모바일 센서 네트워크(MSN)에서 신뢰성 있고 에너지 효율적인 라우팅 기법을 제안한다. 먼저 동적인 네트워크 환경에서 이동성과 패킷발생율에 따른 에너지 소모 패턴 분석을 하였다. 분석된 결과를 바탕으로 제안 기법은 다양한 환경 인자에 따라 에이전트들이 싱크를 중심으로 자율적으로 구성하고 통신한다. 이러한 에이전트 기반의 라우팅 기법은 신뢰성 있고, 에너지 효율적인 통신을 가능하게 한다. 시뮬레이션 결과 제안 기법은 기존 기법에 비해 노드의 이동성의 변화가 심한 환경에서 에너지 효율적임을 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.101-103
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• 1999

Decentralized 정보는 여러 도메인에 대한 heterogeneous한 독립적인 정보가 자율적으로 존재하며 이들 정보간의 관계성의 고려한 전체에 대한 global view가 존재하지 않기 때문에 inter-domain에 대한 마이닝을 수행하는데 어려움이 있다. 본 연구에서는 intra-domain knowledge discovery, intra 및 inter-domain problem solving method라는 접근방법으로, decentralized 데이터 환경에서 문제 해결에 필요한 정보 추출을 위한 데이터 tailoring과 분산 데이터에 대한 목표-지향 데이터마이닝(goal-oriented data-mining)을 통해 문제 해결을 위해 필요한 지식을 생성하고 이들 간의 관련 정보를 탐색하여 문제를 해결하는 프레임워크를 제안한다. 특히, 생성된 지식간의 협동 문제 처리를 멀티 에이전트 패러다임을 이용하기로 한다. 제안 프레임워크는 산재되어 있는 데이터들로부터 문제 해결에 유용한 지식 차원의 정보를 추출해내고 생성된 지식을 바탕으로 각 도메인 정보에 대한 개별적인 사용뿐 만 아니라 서로 cooperation을 통한 문제 해결을 지원함으로써, 개방된 분산 환경하에 decentralized 되어 있는 여러 도메인 정보를 보다 효율적으로 활용할 수 있는 새로운 형태의 문제 해결 방법이라고 할 수 있다.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.3
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• pp.31-37
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• 2003

With the paradigm shifting from the principal of manufacturing efficiency to business globalism and rapid adaptation to its environments, more and more enterprises are being virtually organized as manufacturing network of different units in web. The formation of these enterprise called as Virtual Enterprise(VE) is becoming a growing trend as enterprises concentrating on core competence and economic benefit. 13us paper proposes multi-agent based task assignment system for VE, which attempts to address the selection of individually managed partners and the task assignment to them A case example is presented to illustrate how the proposed system can assign the task to partners.