A flexible videoconference system based on multiagent framework

We present a design and implementation of a flexible videoconference system (VCS) using multiagent computing technology. The proposed system, we named FVCS, aims to reduce the burden of the users under the operational environment with insufficient computational resources, such as the Internet environment with small-scale computers at homes and offices, by embedding flexibility to the conventional videoconference system. In this paper, we design and implement FVCS with knowledge-based multiagent framework to realize adaptability of FVCS. We also evaluate the adaptability of the prototype systems of FVCS based on an operational situation observed in its experiments. From the result of the experiments, we conclude that the multiagent-based design and implementation is reasonable for construction of FVCS.     

火电厂多代理控制系统

针对火电厂非线性、多变量和多控制目标的特点,设计了一个火电厂多代理控制系统(PPMACS).在PP MACS中,前馈控制代理(FFCAs)采用神经模糊系统进行决策,反馈控制代理(FBCAs)采用基于遗传算法的模糊系统进行决策.优化任务分解代理(OTDAs)通过一个优化代理和一个分解代理来进行多目标优化分解PPMACS的任务.协调代理根据运行条件协调PPMACS的各个代理.仿真结果显示了火电厂多代理控制系统能够实现火电单元机组的多目标运行和大范围负荷跟踪.神经网络、模糊逻辑和遗传算法是PPMACS中的智能代理进行决策的有效工具.     

CAMAS: A cluster-aware multiagent system for attributed graph clustering

Attributed graphs describe nodes via attribute vectors and also relationships between different nodes via edges. To partition nodes into clusters with tighter correlations, an effective way is applying clustering techniques on attributed graphs based on various criteria such as node connectivity and/or attribute similarity. Even though clusters typically form around nodes with tight edges and similar attributes, existing methods have only focused on one of these two data modalities. In this paper, we comprehend each node as an autonomous agent and develop an accurate and scalable multiagent system for extracting overlapping clusters in attributed graphs. First, a kernel function with a tunable bandwidth factor δ is introduced to measure the influence of each agent, and those agents with highest local influence can be viewed as the “leader” agents. Then, a novel local expansion strategy is proposed, which can be applied by each leader agent to absorb the most relevant followers in the graph. Finally, we design the cluster-aware multiagent system (CAMAS), in which agents communicate with each other freely under an efficient communication mechanism. Using the proposed multiagent system, we are able to uncover the optimal overlapping cluster configuration, i.e. nodes within one cluster are not only connected closely with each other but also with similar attributes. Our method is highly efficient, and the computational time is shown that nearly linearly dependent on the number of edges when δ ∈ [0.5, 1). Finally, applications of the proposed method on a variety of synthetic benchmark graphs and real-life attributed graphs are demonstrated to verify the systematic performance.     

Intelligent multiagent application system in an AI system

This article presents an intelligent multiagent application system in AI. The research trend into multiagents is changing from a centralized computing environment to a distributed computing environment. Also, the research into multiagents can be changed to a mobile environment. Initially, the study of multiagents is from research into human modeling. Therefore, we fi rst present a brief concept of a mobile multiagent, and then we present some application areas for mobile multiagents, especially in elearning, bioinformatics, control, and information retrieval, etc. Finally, we present the research theme of multiagents in AI. This work was presented in part at the 12th International Symposium on Articial Life and Robotics, Oita, January 25–27, 2007     

Agent UML notation for multiagent system design

From the earliest days of multiagent system development, the need has existed for both a methodology and a modeling notation that assist in design. The agent UML community has responded by developing the AUML notation - a UML profile dedicated to agents that tries to simplify the transition from software engineering to multiagent system engineering. The idea behind AUML is to exploit UML extension capabilities such as stereotypes and constraints. AUML crystallizes a growing concern for agent-based modeling representations and lets designers move smoothly from software development to agent development.     

面向Web服务的多Agent系统的通信机制

介绍了一种面向Web服务的多Agent系统的通信机制,该通信机制通过消息模板和基于本体论的知识表示语言,消除了多Agent系统中因通信语言的差异而产生的影响,实现了Agent之间的有效通信。     

Context-aware multiagent system: Planning home care tasks

Context-aware systems are able to capture information from the context in which they are executed, assign a meaning to the gathered information, and change their behavior accordingly. As a result, the systems can offer services to users according to their individual situation within the context. This article analyzes the important aspects of context-aware computing such as capturing information for context attributes and determining the manner of interacting with users in the environment. Used in conjunction with mobile devices, context-aware systems are specifically used to improve the usability of applications and services. This article proposes the home care context-aware computing (HoCCAC) multiagent system that identifies and maintains a permanent fix on the location of patients in their home, and manages the infrastructure of services within their environment securely and reliably by processing and reasoning the data received. Based on the multiagent system, a prototype was developed to monitor patients in their home. The HoCCAC multiagent system uses a critical path method-based planning model that, in the present study, prepares the most optimal task-planning schedule for the patients in their home, is capable of reacting automatically when faced with dangerous or emergency situations, replanning any plans in progress and sending alert messages to the system. The results obtained with this prototype are presented in this article.     

Holonic job shop scheduling using a multiagent system

Manufacturing job shop scheduling is a notoriously difficult problem that lends itself to various approaches - from optimal algorithms to suboptimal heuristics. We combined popular heuristic job shop-scheduling approaches with emerging AI techniques to create a dynamic and responsive scheduler. We fashioned our job shop scheduler's architecture around recent holonic manufacturing systems architectures and implemented our system using multiagent systems. Our scheduling approach is based on evolutionary algorithms but differs from common approaches by evolving the scheduler rather than the schedule. A holonic, multiagent systems approach to manufacturing job shop scheduling evolves the schedule creation rules rather than the schedule itself. The authors test their approach using a benchmark agent-based scheduling problem and compare performance results with other heuristic-scheduling approaches.     

A multiagent dynamic interaction testbed: Theoretic framework, system architecture and experimentation

Recent research on Distributed Artificial Intelligence(DAI)has focused upon agents‘ interaction in Multiagent System.sThis paper presents a text understanding oriented multiagent dynamic interaction testbed(TUMIT);the theoretic framework based upon game theory,the free-market-like system marchitecture,and experimentation on TUMIT.Unlike other DAI testbeds,TUMIT views different text understanding(TU)methods as different“computational resources”,and makes agents choose different TU paths and computational resources according to the resouce information on the bulletins in their hostcomputer.Therefore,in TUMIT,task allocation is wholly distributed.This makes TUMIT work like a “free market”.In such a system,agents‘choices and resource load may oscillate.It is shown theoretically and experimentally that if agents use multi-level of “history information”,their behavior will tend to converge to a Nash equilibrium situation;and that if agents use “fecall-forget” strategy on “history information”,the convergence can be accelerated and the agents can acclimate themselves to changed environment.Compared with other DAI testbeds,TUMIT is more distributed,and the agents in TUMIT are more adaptive to the dynamic environment.     

A platform-independent metamodel for multiagent systems

Various agent-oriented methodologies and metamodels exist to design and develop multiagent systems (MAS) in an abstract manner. Frequently, these frameworks specialise on particular parts of the MAS and only few works have been invested to derive a common standardisation. This limits the impact of agent-related systems in commercial applications. In this paper, we present a metamodel for agent systems that abstracts from existing agent-oriented methodologies, programming languages, and platforms and could thus be considered as platform-independent. This metamodel defines the abstract syntax of a proposed domain-specific modelling language for MAS that is currently under development and provides furthermore the base to generate code out of the generated designs. This is done by applying the principles of model-driven development (MDD) and providing two model transformations that allow transforming the generated models into textual code that can be executed with JACK and JADE.     

ExperNet: an intelligent multiagent system for WAN management

The authors describe ExperNet, an intelligent multiagent system developed to assist in managing large-scale data networks. The system assists network operators at various nodes of a WAN to detect and diagnose hardware failures and network traffic problems, suggesting the most feasible solution through a Web-based interface.     

A modern approach to multiagent development

Multiagent systems (MAS) development frameworks aim at facilitating the development and administration of agent-based applications. Currently relevant tools, such as JADE, offer huge possibilities but they are generally linked to a specific technology (commonly Java). This fact may limit some application domains when deploying MAS, such as low efficiency or programming language restrictions. To contribute to the evolution of multiagent development tools and to overcome these constraints, we introduce a multiagent platform based on the FIPA standards and built on top of a modern object-oriented middleware. Experimental results prove the scalability and the short response-time of the proposal and justify the design and development of modern tools to contribute the multiagent technology.     

A multiagent architecture for fuzzy modeling

In this paper a hybrid learning system that combines different fuzzy modeling techniques is being investigated. In order to implement the different methods, we propose the use of intelligent agents, which collaborate by means of a multiagent architecture. This approach, involving agents which embody the different problem solving methods, is a potentially useful strategy for enhancing the power of fuzzy modeling systems. ©1999 John Wiley & Sons, Inc.     

Improving the robustness of GNP-PCA using the multiagent system

In order to improve the robustness of Genetic Network Programming fuzzy data mining and PCA (GNP-PCA) based face recognition in the Gaussian and Salt&Pepper noisy testing environments, a GNP-based multi-agent system is constructed using GNP-PCA and multi-resolution analysis in this paper. In the proposed approach, the different scales of training images in the Laplacian pyramid are regarded as sub-environments and each GNP-PCA is performed as an agent in its corresponding environment. Face recognition is finally realized by maximizing the weighted average matching degrees of all the persons in the training database. Experimental results indicate that the proposed method has improved the robustness of GNP-PCA in the Gaussian and Salt&Pepper noisy testing environments considerably.     

Amacoia a multiagent system for designing flexible assembly lines

AMACOIA (approche multiagents pour la conception d'installations d'assemblage) is a tool for designing flexible assembly lines. Its objective is to determine the functional specifications of the equipment (manipulators, fixtures, grippers, conveyors, etc.) and the organizational structure (cells, workstations) for the least expensive assembly line able to assemble a product (or a family of products) within the contract cycle time. The design process is implemented by two interacting multiagent systems. One searches through the assembly sequence space, and the other searches through the assembly line space. The resulting assembly line is built by negotiation and self-organization of the agents. Both multiagent systems are regulated by simulated annealing to optimize the technological cost of the assembly line.     

Design and implementation of a multiagent stock trading system

Stock trading is one of the key items in an economy and estimating its behavior and taking the best decision in it are among the most challenging issues. Solutions based on intelligent agent systems are proposed to cope with those challenges. Agents in a multiagent system (MAS) can share a common goal or they can pursue their own interests. That nature of MASs exactly fits the requirements of a free market economy. Although existing studies include noteworthy proposals on agent‐based market simulation and researchers discuss theoretical design issues of agent‐based stock exchange systems, unfortunately only a very few of the studies consider exact development and implementation of multiagent stock trading systems within the software engineering perspective and guides to the software engineers for constructing such software systems starting from scratch. To fill this gap, in this paper, we discuss the development of a multiagent‐based stock trading system by taking into consideration software design according to a well‐defined agent oriented software engineering methodology and implementation with a widely‐used MAS software development framework. Each participant in the system is first designed as belief–desire–intention agents with their facts, goals, and plans, and then belief–desire–intention reasoning and behavioral structure of the designed agents are implemented. Lessons learned during design and development within the software engineering perspective and evaluation of the implemented multiagent stock exchange system are also reported. Copyright © 2011 John Wiley & Sons, Ltd.     

A multiagent framework for automated online bargaining

Automated online bargaining is currently limited by fixed price-issuing capabilities. The authors propose a multi-agent framework for automated bargaining that uses pattern matching and a dynamic price-issuing agent to better serve customers and the seller's bottom line.     

MASCEM: a multiagent system that simulates competitive electricity markets

Around the world, the electricity industry, which has long been dominated by vertically integrated utilities, is experiencing major changes in the structure of its markets and regulations. Owing to new regulations, it's evolving into a distributed industry in which market forces drive electricity's price. The industry is becoming competitive; a market environment is replacing the traditional centralized-operation approach. This transformation is often called the deregulation of the electricity market. MASCEM, a multiagent simulator system, is a valuable framework for evaluating new rules, new behavior, and new participants in the numerous electricity markets that are moving toward liberalization and competition.