Earticle

본 논문에서는 모바일 교육 증강현실 게임 환경에서 게임 학습 참여자의 학습 필요와 요구에 부응하 는 어휘를 자동으로 제공해주는 지능형 어휘 추천 에이전트를 제안한다. 제안된 에이전트는 모바일 기술의 특 성과 증강 현실 기술의 특성을 최대한 반영하여 설계하도록 하고 상황 어휘 추론 모듈, 싱글 게임 어휘 추천 모듈, 배틀 게임 어휘 추천 모듈, 학습 어휘 목록 모듈, 유의어 모듈로 구성한다. 연구 결과, 게임 학습 참여자 들은 대체적으로 만족함을 알 수 있다. 상황 어휘 추론과 유의어의 정확도는 각각 4.01점, 4.11점으로 게임 학습 참여자가 처한 상황과 관련이 깊은 어휘가 추출되는 것을 보여준다. 하지만 만족도의 경우에는 배틀 게임 어휘 (3.86)는 개인별 학습자의 추천 어휘 중에서 공동으로 사용할 수 있는 어휘를 추천하기 때문에 싱글 게임 어휘 (3.94)보다는 상대적으로 낮은 결과가 나타났다.

In this paper, we propose an intelligent vocabulary recommendation agent that automatically provides vocabulary corresponding to game-based learners' needs and requirements in the mobile education augmented reality game environment. The proposed agent reflects the characteristics of mobile technology and augmented reality technology as much as possible. In addition, this agent includes a vocabulary reasoning module, a single game vocabulary recommendation module, a battle game vocabulary recommendation module, a learning vocabulary list Module, and a thesaurus module. As a result, game-based learners' are generally satisfied. The precision of context vocabulary reasoning and thesaurus is 4.01 and 4.11, respectively, which shows that vocabulary related to situation of game-based learner is extracted. However, In the case of satisfaction, battle game vocabulary(3.86) is relatively low compared to single game vocabulary(3.94) because it recommends vocabulary that can be used jointly among recommendation vocabulary of individual learners.

Rapidly growing innovative technologies enabled human beings to enjoy smart city services despite the development of such cities are still facing several challenges needed to be addressed. The waste management in smart cities particularly its segregation by smart methods is one of the primary concerns as the amount of waste generated every day by citizens is increasing. A comprehensive intelligent waste management system is direly needed to address the situation. This article aims to segregate recyclable and non-recyclable types of garbage collected from smart cities using the Intelligent Agent proposed and developed so far. The expected smart solution should provide the best level of accuracy at the lowest possible cost. Our study proposed a model to differentiate and segregate waste into recyclable and organic objects based on Intelligent Agent developed using a Convolutional Neural Network (CNN). The model proposed comprises of Intelligent Agent developed and the existing CNN model which is commonly used for transfer learning. The classification accuracy achieved is up to 93.27% which is better than the already published results of different models discussed in the recent past research studies. Furthermore, how can recyclable and organic waste be utilized in the future is part of our ongoing study. The findings may be of interest to practitioners and the researchers’ community working in the relevant field.

인공지능 기술이 발전함에 따라 자동적인 상황 판단과 능동적인 작업수행이 가능한 지능형 에이전트(Intelligence Agent)는 현재 다양한 상황에서 사용자를 보조해주는 개인 비서로서의 역할을 수행 하고 있다. 본 연구는 지능형 에이전트가 사용자의 의사결정이나 특정 목표 달성에 도움을 주는 조력자로서 다양하게 활용되고 있다는 점에 주목하여, 커뮤니케이션 방식에 따라 지능형 에이전트가 제공하는 제안 수용이 달라지는지 확인하였다. 구체적으로, 장기적 목표를 추구하는 과정에서 목표에 실패할 수 있는 상황을 두 가지로 구분하여 지능형 에이전트의 커뮤니케이션 방식이 제안 수용의도에 미치는 효과를 확인하였다. 연구 결과, 부적합 행동 선택 상황에서 지능형 에이전트의 Don’t 커뮤니케이션 방식이 Can’t 커뮤니케이션 방식에 비해 제안 수용의도가 더 높게 나타났다. 반면, 적합 행동 지연 상황에서는 두 방식 간 차이는 없었다. 본 연구는 장기목표 추구 상황에서 사용자가 목표에 부적합한 행동을 할 때 지능형 에이전트의 커뮤니케이션 영향을 확인하였다는 것에 의의가 있으며, 지각된 통제감을 기반으로 인공지능 비서의 제안에 대한 수용의도를 높일 수 있는 커뮤니케이션 방식을 밝혀냈다는 실무적 시사점을 제공한다.

With the advancement of artificial intelligence technology, intelligent agents capable of understanding natural speech, automatically assessing situations, and actively performing tasks are now serving as personal assistants in various contexts. Recognizing that intelligent agents serve as aides in decision-making and goal achievement, this study explores whether the acceptance of suggestions from these agents varies based on communication methods. Specifically, we investigated how an intelligent agent’s communication method affects intentions to accept suggestions in situations where the agent may struggle to achieve a long-term goal. The findings revealed that the ‘Don’t’ communication method of the intelligent agent was associated with a higher intention to accept suggestions compared to the ‘Can’t’ communication method in scenarios involving non-conforming behavior choices. However, there was no discernible difference between the two methods in situations where delayed action conforming to the goal was required. This study is significant as it underscores the impact of communication methods employed by intelligent agents when users engage in goal-inappropriate behavior during long-term goal pursuits.

ERP가 기업의 정보를 통합하는데 유용한 시스템이라는 것에는 의심할 바가 없다. 그러나 많은 회사들은 ERP시스템을 도입하는 것을 주저하고 있다. 주요한 이유는 유지보수를 포함한 ERP 구축에 엄청난 총비용이 들기 때문이다. 특별히 프로세스를 리엔지니어링(reengineering)하거나, 시스템이 각 기업에 경영지식을 담도록 하는 맞춤화 작업이 적절한 시간에 수정 보완되어지지 않는다면 ERP가 내놓는 성과의 최적화의 결과를 보장할 수 없다. 그러므로 본 논문은 ERP가 내놓는 성과의 최적화의 결과를 보장할 수 없다. 그러므로 본 논문은 ERP시스템의 사용자와 데이터베이스에 의해 발생되는 환경적 변화 그 자체를 수용하는 ERP시스템을 구축하는데 목표를 두고 있다. ERP시스템이 최적의 성과를 유지하도록 하기 위해 페트리넷 이론(petri-net theory)과 지능형 성과를 에이전트기술(Intelligent agent technology)을 사용하였다. 지능형 에이전트는 데이터베이스를 통합하고 예외적인 변화 상황을 발견하며, 그 변화가 ERP시스템의 성과에 어떤 영향을 주게 되는지에 대해서 각 에이전트끼리 자동적으로 협동하게 된다. 해당 에이전트의 역동성은 페트리넷으로 표현되었다. 새롭게 제안된 ERP시스템은 BPR(business process reenginnering)의 프로세스들을 최적 상태로 유지하도록 만들 것이다. 마지막으로 제안된 ERP 유지 보수 시스템의 타당성을 설명적인 물류부분 사례를 통해 제시하였다.

Even though there is no doubt that ERP(Enterprise Resource Planing) system is a prevailing solution for integrating corporate information, many companies still hesitate adopting ERP system because of a great deal of cost including maintenance cost. In special, unless consulting knowledge that is infused into process reengineering phase or adequately embedded in customized ERP system is upgraded on time, then we cannot guarantee the optimality of system performance. Hence, this paper aims to construct an ERP system that adapts itself to environmental changes that are issued by database and users. To do so, we adopt intelligent agent technology and Petri net theory. The agents autonomously cooperate each other to investigate databases and to find any exceptional changes and analyze how the changes will affect ERP performance. The dynamics of the agents are represented as Petri nets. The newly proposed ERP system is to make corresponding BPR processes maintain optimality. To show the feasibility of the proposed ERP maintenance system, logistics component is described as an illustrative example.

금속 부품의 부식과 내구성 강화를 위해서 페인트를 이용하여 표면처리 하는 전착도장 업체의 생산 공정과 작업환경 은 전근대적인 방식에서 벗어나지 못하고 있다. 이러한 뿌리산업체의 문제점은 품질 저하, 생산성 저하, 환경오염 등으로 종사자의 기피현상이 가증되고 있어 공장자동화 차원의 개선이 시급하다. 따라서 본 논문은 전착도장 업체가 안고 있는 문제점을 해소하고자 전착도장 공정 제어시스템을 IoT 기반의 스마트 팩토리화 하고, 지능형 센싱 에이 전트, 생산관리시스템 연동 API, 자동제어장치가 산업기술용 네트워크로 연결된 새로운 기술을 제안하고 실증 수준 의 시험평가를 실시하여 그 효용성을 입증하였다.

The production process and working environment of electrodeposition coating companies that surface-treat with paint to enhance corrosion and durability of metal parts have not escaped from the pre-modern way. The problems of the root industry are that the evasion of workers is increasing due to the deterioration of quality, productivity, and environmental pollution, so it is urgent to improve the level of factory automation. Therefore, in this paper, the electrodeposition coating process control system is an IoT-based smart factory to solve the problems of the electrodeposition coating company, and an intelligent sensing agent, production management system interworking API, and automatic control device are connected to the industrial technology network. And proved its effectiveness by conducting empirical level test evaluation.

Human emotion is a point to communicate each other. we applied human emotion to make computer interface. In this paper, we have researched the next generation interface technology "Bio Feedback system" which enabled us to estimate an emotional state of human being and so we applied it to embody ‘NPC’ using "Reinforcement Learning Algorithm" which is able to respond appropriately to human being’s emotions.

최근 개발자와 사용자 사이에서 게임의 인공지능에 대한 관심이 늘어나고 있다. 그래픽과 사운드 요소의 한계로 인 해 화려하고 웅장한 게임보다 다양한 재미를 줄 수 있는 게임을 원하기 때문이다. 기존의 게임 인공지능 기법은 단순 하여 쉽게 질리고, 사용자에게 다양한 재미를 지속적으로 제공하기 어렵다. 그러나 학습 능력을 갖춘 게임은 다양하고 예측하기 어려운 특성으로 인해 사용자에게 끝없는 재미를 제공할 수 있다. 본 논문에서는 이러한 학습 능력을 게임 내의 에이전트에게 부여하기 위해 심리학 이론 중 하나인 관찰학습 이론을 적용한 알고리즘을 제시하고자 한다.

E-learning is the internet enabled learning. Internet has ongoing to restructuring education. Intelligent agent based e-learning provide a common infrastructure to assimilate varied software components. There are two sorts of e-learning synchronous and asynchronous. This paper described the detail of well-known agent based architecture for e-learning. E-learning is going to be gigantic. There are multiple benefits of e-learning; it is convenient, self-service mix, match, on demand any time anyplace, private learning, Self-paced and elastic. E-learning provide cost effective and virtual environment. E-learning gives the ability to user to collect the quantifiable and sensible material, examine, and distribute and custom e-learning knowledge from multiple e learning sources.

Synchrotron radiation (SR) sources provide very high photon flux light in a very narrow opening angle with wavelength ranging from visible to hard X-rays for use in experiments related to material science, physics, chemistry and biology. In the beam lines (BL) the SR position is highly dependent on the electron beam position and angle at the source point. The tuning of accelerator for getting the desired electron beam position and angle at the source point is a time consuming and regular job done during commissioning of new BL or when accelerator is operated at new operating point. This paper presents a novel intelligent agent based operator support and beam orbit control scheme for accelerator control. The proposed multi-agent based scheme is well suited for the multilayer control system architectures of synchrotron radiation sources. The scheme successfully distributes the orbit control job to multiple low complexity reactive agents that work simultaneously and control the local orbit for individual BL and insertion devices (ID) in an optimized manner. The proposed scheme of beam orbit control in particular is very useful for machines like INDUS-2, where new BL are in the process of commissioning as this scheme reduces the operator efforts and accelerator tuning time for providing beam to new BL. It also extends the beam availability to other BL (already installed and in use) as the agent tunes the accelerator in systematic way and under constraints on local orbit bump leakage thereby enabling the use of other BL for routine experiments which otherwise was not possible. The effectiveness of the scheme is shown through simulation results obtained by applying the stated scheme on INDUS-2 storage ring model.

Quality of Service (QoS) support in Mobile Ad hoc NETworks (MANETs) is a challenging task due to bandwidth and delay constraints, varying channel conditions, power limitations, node mobility and dynamic topology. This paper proposes an intelligent agent based on-demand (source initiated) delay aware QoS routing scheme in MANETs by using software agents that employ neuro-fuzzy logic supported by Q-learning. The proposed scheme operates in following steps. (1) Source node uses an agent that uses Dynamic Source Routing (DSR) to find various paths, their bandwidth and packet loss rate to reach a destination. (2) Parallely, a static neuro-fuzzy agent at the source node is used to optimize membership functions of fuzzy parameters according to user delay requirement of the fuzzy inference system (FIS); also, a fuzzy Q-learning static agent at the source node is employed to optimize the consequent part of if-then rules of FIS. (3) Fuzzy static agent at the source node decides whether node on a path satisfies delay requirement for an application according to the user by considering fuzzy parameters, bandwidth, packet loss rate and delay. (4) A path with QoS satisfied nodes will be selected by fuzzy QoS static agent and (5) mobile agents are used to maintain and repair the path. The scheme has been simulated in various network scenarios to test operation effectiveness and observed that proposed scheme performs better than the existing fuzzy based DSR routing methods.

In large-scale grid platforms, providing fault-tolerance for users is always a challenging task because of the uncertainty of network resources. In this paper, we present an intelligent agent based meta-scheduler, which is aiming at improving the fault-tolerance of grid systems when running user’s application. The proposed meta-scheduler is designed as an extendable framework, which allows plugging in multiple scheduling policies for deal with different scenarios. The agent-based scheduling framework enables grid systems to deploy their local schedulers in a flexible manner. Extensive experiments are conducted to investigate the performance of the proposed meta-scheduler, and the results show that it is effective to provide enhanced dependability for grid users, especially when the system is across multi-organization.

In this paper, multi-intelligent agent architecture has been proposed for automatic knowledge extraction from its resources (domain experts and text documents). The extracted knowledge should be stored in a knowledge base to be used later by knowledge-based systems. This article aims to produce an effective knowledge base by cooperation between expert mining and text mining techniques. Firstly, we are constructing an Expert Mining Intelligent Agent (EMIA) able to interview with domain experts for mining problem solving knowledge as production rules in a specific diagnosis domain. It is also responsible for extracting the patterns or linguistic expressions and save it in a conceptual database. Secondly, we are constructing a Text Mining Intelligent Agent (TMIA) capable of extracting production rules from a text document corpus. The achievement of that extraction can be performed by a text document categorization based on a traditional term weighting scheme (TF-IDF) and using the Stanford parser to analyze and produce a parsing tree for each sentence in that document. Then, the TMIA looks for all causal words and takes them as separation words to generate patterns and sub-patterns based on the conceptual database. Finally, the TMIA stores those patterns and sub-patterns in a pre-formatted template and displays it to a domain expert for a modification process to construct accurate production rule.

From the perspective of modern cognitive science, firstly the theoretical model of network teaching is analyzed, and then an individualized network teaching model based on intelligent agent technology. Finally, implementation strategies of individualized network teaching will be discussed.

The performance of traffic systems is greatly dependent on their ability to react to changing traffic patterns and different situations. On traditional traffic systems, the lights run green for fixed intervals of time no matter what the density of the traffic is. Here, we implement an intelligent-agent traffic model that controls the amount of time a light runs green for, based on the number of cars (density) standing in the light.

Recently, Since people always carry mobile devices and can learn whenever and wherever by using not only phone, text, SNS, and TV, but also using learning device, recorder, e-book, e-dictionary, and internet surfing due to rapid distribution of smartphone, the interest in intelligent learning agent related to situated learning is increasing more. The method of practicing the situated learning theories developed so far provides learning contents to learners by analyzing their environment information along with their basic information. However, if learning contents are recommended by considering only basic and partial information of a learner, in many cases, they do not match with the demand from the learner, so it could become factors that decrease learner’s learning desire and obstruct inducing learning motivation. Therefore, this study propose an intelligent learning agent that automatically recommends learning contents that learners want in timely manner in their real life by analyzing not only learners’ location information, but also their available time for learning and Internet activity pattern.