Earticle

On the basis of concept, characteristics and related work of collaborative learning, this paper proposes an intelligent web collaborative learning system prototype based on multi-agent technology. At first, the paper introduces a collaborative learning framework which supporting group learning and illustrates the whole learning process. Secondly, the paper presents some key issues to implementation of the virtual learning environment, such as intelligent grouping, automatic question answering, knowledge management, and opinion mining. Preliminary learning practice shows that it is a practical and efficient way.

This paper proposes a multi-agent technology to energy management of the distributed generations in a micro-grid (MG) system. An introduction and analysis of the distributed intelligent multi-agent technology are presented, including its architecture and operation mechanism. Then, the objective function about minimizing the operating cost and environment losses taking in electricity trading is given. An implementation of Central Force Optimization (CFO) utilizing variable initial probes and decision space adaptation is presented, and Compare it to particle swarm optimization (PSO) method. The daily load forecast of electric power system is presented. The simulation results demonstrate that the proposed multi-agent technology is successfully monitored, controlled and operated for the energy management of the distributed generations in micro-grid system.

Considering the complexity of flexible workshop scheduling, combined with plant production process characteristics and constraints, we constructed a multi-agent system model to solve multi-objective flexible workshop scheduling problems. This paper proposed an algorithm which was a combination of the ant colony algorithm and Q-learning algorithm. This paper also analyzed and implemented how to solve the workshop scheduling optimization problem. Finally, this paper proved the validity of methods to solve the multi-objective flexible workshop scheduling optimization problems with examples on JADE platform.

Recently, decentralized approaches to artificial intelligence (AI) development, such as federated learning are drawing attention as AI development's cost and time inefficiency increase due to explosive data growth and rapid environmental changes. Collaborative AI technology that dynamically organizes collaborative groups between different agents to share data, knowledge, and experience and uses distributed resources to derive enhanced knowledge and analysis models through collaborative learning to solve given problems is an alternative to centralized AI. This article investigates and analyzes recent technologies and applications applicable to the research of multi-agent collaboration of AI bots, which can provide collaborative AI functionality autonomously.

최근 전기 전력 도메인의 IT화 (전력 IT) 가 본격적으로 진행되면서 전기 데이터를 취득하기 위한 기기들 뿐만 아니라 전력 운영 환경이 변화를 도모하고 있다 본 논문에서는 이런 전력 IT 의 일환으로 개발된 지능형 전력 정보 모니터링 시스템 (K-WAMS)을 소개하고자 한다. 이 시스템은 이기종 분산 환경에서 전력 정보를 실시간으로 전송 받아 현재 전력계통의 상황을 직관적으로 판단할 수 있는 정보를 제공하여 광역 정전을 예방하고자 하는 조기경보시스템 (Early Warning System) 이다. 또한, 전력계통의 안정성 여부를 판단하는 지능형 에이전트의 구현을 통해 광역 정전을 예견하는 지수를 제공하고, 실시간 데이터 처리 및 이력 데이터 저장 에이전트들을 통해 현재 취득되고 있는 현장 데이터 모니터링 기능을 제공한다. K-WAMS 는 실시간으로 전송 처리되는 고속 대용량 데이터들의 처리 지연, 동적 환경으로 인한 네트워크의 부하문제를 해결하기 위해서 IPC Shared Memory 기법을 이용하였고, 동적 프로세스들의 작업 스케줄링을 관리하기 위한 IPC Message Queue 기법을 이용하였다. 또한 논리적 기능 기반으로 설계된 현재 중앙 급전소(GCC) 단위의 설계 모델은 향후 지역 급전소(RCC) 단위로 설치 될 경우 재사용 되어 개발 생산성을 향상시킬 것으로 예상된다.