• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.4
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• pp.54-62
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• 2011

Emotional reaction should be different from the purpose of the robot system. The method for emotional reaction is also different from the specification of the robot system. Therefore, emotional behavior decision model, which is applied to social robots regardless of specifications and purposes, is necessary. This paper introduces a universal emotional behavior decision model designed for applying to various social robots that have different specifications and purposes. Multiple emotions, a set of probability value of every emotion, are calculated independently and expressed according to the purpose of the robot system. Then, behavior, for emotional reaction according to the calculated multiple emotions, is decided regarding the specification of the robot system. The decided behavior is a combination of unit behaviors that indicates the smallest expressible behaviors in each expression parts. It is possible to express various undefined behaviors by generating unit behavior combinations according to multiple emotions. The universal emotional behavior decision model is applied to three kinds of social robot systems that have different specifications and purposes.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.149-158
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• 2012

As service-oriented software is considered as one of solutions to fulfill the users' needs in internet service environment, it has been increased the demands of reliable service development by the composition of internet services. However a critical issue in the service development approach is to satisfy the performance requirement as well as the functional correctness for the developing services, because impatient user multiply clicks the request-button of service without a short waiting. This paper proposes a framework to examine the performance of composite service. Our testing framework provides the data of service response time to service developer by measuring the service execution time. We develope an Aspect-based timer service, and weave the service with existing services to measure the execution time. Additionally, we perform some experiments to confirm the usefulness of performance test for composite service. This framework can support to develop a good performance service by substitution of the dragging service with another new service that will be a component of composite service.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.199-214
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• 2007

Recently, there have been a lot of works trying to realize automated composition of semantic web services though application of AI planning techniques. The traditional AI planning techniques, however, have some limitations: it is not easy to represent a web service process with complex control constructs as an action or a plan; it is hardly possible to consider enough the rich information contained in domain ontologies during the planning process; it is impossible to model directly the data flow from the outputs of a web service to the inputs of another web service; it is difficult to predict and deal with uncertainty and dynamics of the environment because the plan generation phase is supposed to be separated from the plan execution phase. In order to overcome some of these limitations, this paper suggests a reactive planning approach to automated composition of semantic web services. Through some experiments using several e-commerce web services, we found that the reactive planning is an effective way to realize automated composition of semantic web services.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.990-993
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• 2019

인공지능(AI)은 최근 다양한 산업과 사회에서 패러다임을 바꾸고 있지만, 최첨단 AI 가 제조업에서는 즉각적인 성과를 보이지 못 하고 있다. 다시 말해, Industry 4.0 시점에서 기존의 접근 방법과 차별화되는 실용적인 방법론이 필요하다. 여기서 중요한 점은 '어떤' 데이터를 '어떻게' 활용하여 '어느' 부분에 적용할 것 인가이다. 제조업은 게임과 같이 가상의 캐릭터가 하나의 객체 단위로 구동되는 것이 아니라 수많은 하드웨어가 물리적으로 조합되어 연동한다. 따라서, 현실 세계에서는 물리적 마모, 고장 등으로 인해 엔지니어의 개입 없이 수천만 번 이상의 반복 학습이 불가능하다. 또, 제조업은 학습을 위한 방대한 양의 데이터를 수집하고 레이블링 하는 것이 매우 어렵다. 이 두 가지 한계를 극복할 수 있는 방법은 현실과 매우 유사한 환경을 시뮬레이션으로 재연 후 강화 학습을 사용하는 것이다. 제조 분야에서 아주 복잡한 환경 중 하나로 이송 설비가 있으며, 본 논문에서는 그리드 분류 시스템을 개발하고 강화 학습을 적용시킬 수 있는 환경을 설계한다.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.