This paper introduces to the reader the field of cooperating intelligent software agents. It first reviews the historical roots of distributed Al, and the main concepts that have been developed in that field since the...
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This paper introduces to the reader the field of cooperating intelligent software agents. It first reviews the historical roots of distributed Al, and the main concepts that have been developed in that field since the end of the seventies, and on which today's agent technology is based upon. The paper then concentrates on the modeling of agents. Starting with a terminological discussion of what agents are, and with an example the main characteristics of cooperating intelligent agents are presented, formalized, and discussed. Based upon this the paper proceeds to issues that are relevant to multiagent systems: coordination, communication, and standardization. The paper concludes with a sum-up of the main results, and and an extensive bibliography.
The increasing globalization of markets has raised the competitive pressure. The urge to adapt to rapidly changing conditions has forced many companies to introduce more flexible organizational forms. An adequate info...
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The increasing globalization of markets has raised the competitive pressure. The urge to adapt to rapidly changing conditions has forced many companies to introduce more flexible organizational forms. An adequate information management has to support these changes. One of the key success factors of a corporation will be the capability to perform high quality decision processes. The workbench TUB-MAGIC has been designed and implemented to support the development of distributed, intelligent decision support systems. They use intelligent agents to assist the decisionmaking process in complex situations. The concepts and the realization of TUB-MAGIC were tested by the development of the application MAGNIFICO. It has been implemented to support a cooperative general financial consulting process of a financial institution, e.g. banking corporations.
In this article, we describe a new approach to applying distributed artificial intelligence techniques to manufacturing processes. The construction of intelligent systems is one of the most important techniques among ...
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In this article, we describe a new approach to applying distributed artificial intelligence techniques to manufacturing processes. The construction of intelligent systems is one of the most important techniques among artificialintelligence research. Our goal is to develop an integrated intelligent system for real time manufacturing processes. An integrated intelligent system is a large knowledge integration environment that consists of several symbolic reasoning systems (expert systems) and numerical computation packages. These software programs are controlled by a meta-system which manages the selection, operation and communication of these programs. A meta-system can be implemented in different language environments and applied to many disciplines. This new architecture can serve as a universal configuration to develop high performance intelligent systems for many complicated industrial applications in real world domains.
作者：
Cho, YoungUniv Suwon
Dept Comp Sci San 2-2 Hwaseong Si 445743 Gyeonggi Do South Korea
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 rese...
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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 first present a brief concept of a mobile multiagent, and then we present some application areas for mobile multiagents, especially in e-learning, bioinformatics, control, and information retrieval, etc. Finally, we present the research theme of multiagents in AI.
Traditional Enterprise Resource Planning (ERP) systems strongly focus on planning internal production and logistics disregarding external effects on upstream and downstream contractors in the value chain. Supply Chain...
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Traditional Enterprise Resource Planning (ERP) systems strongly focus on planning internal production and logistics disregarding external effects on upstream and downstream contractors in the value chain. Supply Chain Management accounts for these effects: but, on the other hand, often fails due to the complexity of global planning of the entire supply network. Our paper presents existing approaches using so called Multi Agent Systems, keeping the natural decentralized structure of the problem and locusing on the interaction protocols of the software agents. We present a framework for the integration of these concepts in an open Multi Agent Architecture and discuss dispositive negotiation protocols based on the propagation of time-dependent supply and demand functions.
The concept of trust as presented here focuses on the trustworthiness, or reliability, of information and information sources. Decision makers, or agents, can create judgments based on previous experience with other a...
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The concept of trust as presented here focuses on the trustworthiness, or reliability, of information and information sources. Decision makers, or agents, can create judgments based on previous experience with other agents and by reputation information received from allied agents. These judgments, or trust assessments, are used to predict the behavior of other agents and analyze the trustworthiness, truthfulness, or quality of information. Research concepts have been developed within the trust community, and they are most commonly applied to multi-agent systems research. This work attempts to show that trust research can be directly applied to security problems. Modern trust concepts enforce soft security which can be applied in addition to conventional security methods to build a more robust secure system. This article examines two trust based techniques and demonstrates their basic effectiveness using empirical experimentation. These techniques are then applied in a case study drawn from a more robust domain concerning confidential message transmission. The benefits of applying trust-based techniques to secure a system are measurable, and the costs associated with such techniques are scalable to even the most resource constrained systems.
Multi-Agent Intelligent Systems (MAIS) are loosely-coupled network of problem solving systems that, whenever needed, work together with each other to dynamically solve problems that none of the system can individually...
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Multi-Agent Intelligent Systems (MAIS) are loosely-coupled network of problem solving systems that, whenever needed, work together with each other to dynamically solve problems that none of the system can individually solve. Among the advantages of the MAIS, when compared to the centralized systems, are increased reliability, faster problem solving, decreased communication, and more flexibility. Learning to coordinate the actions is one of the most important task in MAIS. In the current research, we use a widely reported dynamic job shop scheduling simulation model that uses distributed genetic learning of job scheduling strategies (Pendharkar, P.C., 1997. Doctoral Dissertation, Graduate School, Southern Illinois University at Carbondale;Pendharkar, P.C., 1998. distributed learning of objectives for adaptive scheduling (in review);Pendharkar, P.C., Bhattacharyya, S., 1997. Multi-agent learning in distributed artificial intelligence. Proc. 2nd INFORMS Conference on Information Systems and Technology. San Diego, CA, p.156-163;Bhattacharyya, S., Koehler, G.J., 1997. Learning by objectives for adaptive shop-floor learning. Decision Sciences (to appear). Aytug, H., Koehler, G.J., Snowdon, J.L., 1994. Genetic learning of dynamic scheduling within a simulation environment, Computers and Operations Research, 21 (8), 909-925;Aytug, H., Bhattacharyya, S., Koehler, G.J., Snowdon, J.L., 1994. A review of machine learning in scheduling, IEEE Transactions on Engineering Management 41 (2)) and study the performance and design issues in multi-agents information systems for dynamic scheduling in manufacturing. Among the design issue and performance issues considered in this research are coordination between agents, number of agents, and frequency of learning. Our results indicate that coordination between agents, and learning frequency play a significant role in the performance of multi-agent intelligent systems. (C) 1999 Elsevier Science Ltd. All rights reserved.
A formal framework is presented that models communication and promises in multi-agent interactions. This framework generalizes previous work on co-operation without communication (Genesereth et al. 1984a, Genesereth e...
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A formal framework is presented that models communication and promises in multi-agent interactions. This framework generalizes previous work on co-operation without communication (Genesereth et al. 1984a, Genesereth et al. 1986), and shows the ability of communication to resolve conflicts among agents having disparate goals. Using a one-phase deal-making mechanism, agents are able to coordinate and cooperate more easily than in the communication-free model. In addition, there are certain types of interactions where communication makes possible mutually beneficial activity that is otherwise impossible to coordinate.
Over the past five years, the subject of Intelligent Network (IN) focused on architecture and introduction of new services. In parallel, TMN was introduced as a generic management-oriented architecture to be used for ...
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Over the past five years, the subject of Intelligent Network (IN) focused on architecture and introduction of new services. In parallel, TMN was introduced as a generic management-oriented architecture to be used for all kinds of management services. The integration of IN and TMN is a candidate to solve the problem of management of the IN. Unfortunately, this concept of integration is not sufficient to bring a flexible and adaptable management system. This paper describes a model based on distributed artificial intelligence concepts able to manage the Intelligent Network. This model can be integrated in the different layers of the Intelligent Network architecture. We investigate four possibilities: at the physical plane, at the distributed functional plane, at the global functional plane and at the service plane. We compare the advantages and drawbacks of implementing the management process at those levels.
Recent progress in computing hardware technology has resulted in advances in the development of parallel computer systems. This new generation of computers offers advanced architectures and technologies for many AI ap...
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Recent progress in computing hardware technology has resulted in advances in the development of parallel computer systems. This new generation of computers offers advanced architectures and technologies for many AI applications. A new subfield of AI, called distributed artificial intelligence, has emerged which is concerned with the co-operation solution of problems by a decentralized and loosely coupled collection of intelligent agents. In the paper, a parallel expert system called HOPES (Hierarchically Organized Parallel Expert System), is introduced. The system structure and multiblackboard architecture are presented and discussed. The focus of the paper will be on the implementation issues of the HOPES system. Although the so-called second generation expert systems technology has been around for some time, relatively little research effort has been put on implementing such systems. Thus, a major purpose of the paper is to provide general guidelines for implementations of parallel knowledge-based systems. The authors are concerned with two categories of hardware structures. First, multiprocessor system with common memory. Second, multiprocessor system without common memory. This paper will reveal some very important implementation problems and discuss key issues which are believed to be essential for implementing parallel/distributed knowledge-based systems.
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