Transforming standalone expert systems into a community of cooperating agents

Jennings, Nicholas R.; Varga, László Zsolt; Aarnts, R. P.; Fuchs, J.; Skarek, P.

doi:10.1016/0952-1976(93)90016-q

Cited by 48 publications

(32 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In such cases, when interdependent problems arise the agents have to interact with one another to ensure their dependencies are properly managed. Real world examples of such domains for which agent based systems have been developed include: power systems management Varga et al, 1994), particle accelerator control (Jennings et al, 1993), telecommunications network management (Weihmayer and Velthuijsen, 1994), spacecraft control (Schwuttke and Quan, 1993), computer integrated manufacturing (Parunak, 1995), transportation management (Fischer et al, 1995), job shop scheduling (Morley and Schelberg, 1993) and steel coil processing (Mori et al, 1988). …”

Section: Applying Agent Technologymentioning

confidence: 99%

“…Therefore, in the long term, the only way such legacy systems can remain useful is to incorporate them into a wider cooperating community (a cooperative information system (Papazoglou et al, 1992)) in which they can be exploited by other pieces of software. This can be done, for example, by building an ''agent wrapper'' around the software to enable it to interoperate with other systems (Genesereth and Ketchpel, 1994;Jennings et al, 1993).…”

Section: Applying Agent Technologymentioning

confidence: 99%

See 1 more Smart Citation

Applying Agent Technology

Jennings

Wooldridge

1995

Applied Artificial Intelligence

153

View full text Add to dashboard Cite

The term "agent", (and hence "agent based computing", "agent based system", ''multi-agent system''), is being increasingly used within information technology to describe a broad range of computational entities. These entities range from relatively simple systems, (such as Microsoft's TIP WIZARD, which provides advice to users working in EXCEL 5, or desktop agents which prioritise and filter electronic mail (Maes, 1994)), right up to very large, interoperable expert systems or databases which contain thousands of lines of code (e.g., ARCHON (Jennings et al., 1995) and Carnot (Huhns et al., 1992)). Broadly speaking, we can identify three different classes of agent. At the simplest level, there are "gopher" agents, which can execute straightforward tasks based on pre-specified rules and assumptions (e.g., remind me that I have a lecture every Monday this term at 2:00). The next level of sophistication involves "service performing" agents, which execute a well-defined high-level task at the request of a user (e.g., arrange a meeting or find an appropriate flight). Finally, there are "predictive/proactive" agents, which volunteer information or services to a user, without being

show abstract

Section: Applying Agent Technologymentioning

confidence: 99%

Section: Applying Agent Technologymentioning

confidence: 99%

Applying Agent Technology

Jennings

Wooldridge

1995

Applied Artificial Intelligence

153

View full text Add to dashboard Cite

show abstract

“…Such resources may be "wrapped up" as agents to provide this flexible and consistent interface [7,8].…”

Section: The Role Of Agents In the Virtual Laboratorymentioning

confidence: 99%

Constructing a virtual training laboratory using intelligent agents

Norman¹,

Jennings²

2002

IJCEELL

View full text Add to dashboard Cite

This paper reports on the results and experiences of the Trilogy project; a collaborative project concerned with the development of a virtual research laboratory using intelligent agents. This laboratory is designed to support the training of research students in telecommunications traffic engineering.Training research students involves a number of basic activities. They may seek guidance from, or exchange ideas with, more experienced colleagues. High quality academic papers, books and research reports provide a sound basis for developing and maintaining a good understanding of an area of research. Experimental tools enable new ideas to be evaluated, and hypotheses tested. These three components-collaboration, information and experimentation-are central to any research activity, and a good training environment for research should integrate them in a seamless fashion. To this end, we describe the design and implementation of an agent-based virtual laboratory.

show abstract

“…An early prototype of the PCM, called GRATE [12], which implemented the SAM and the CM as concurrent processes, was built for evaluation purposes and applied to the domains of electricity transportation management [12] and particle accelerator control [9]. As a consequence of this prototyping activity, three important points pertaining to the design of the PCM were highlighted [23].…”

Section: Design Decisionsmentioning

confidence: 99%

Designing a reusable co-ordination module for co-operative industrial control applications

Jennings

Pople

Mamdani

1996

IEE Proceedings - Control Theory and Applications

Self Cite

View full text Add to dashboard Cite

Distributed artificial intelligence (DAI) systems, in which multiple agents communicate and co-operate with one another to achieve their individual and collective goals, are a promising enabling technology for constructing large, realworld industrial control applications. To facilitate the development of such systems a number of generic DAI frameworks have been devised. These frameworks typically aid the development process by providing a language, a set of structures, and/or some tools with which the necessary infrastructure and support mechanisms for interacting agents can be instantiated. The paper reports on one such framework, called ARCHONTM, which has been used to build DAI systems in the following industrial control domains: electricity distribution management, electricity transportation management, cement factory control, particle accelerator control and flexible assembly robotic cells. A distinguishing and novel feature of the ARCHON framework is that it extends the level of support offered to the system builder ~ it provides generic and reusable knowledge about the process of co-operation, in addition to the more standard development facilities. This generic knowledge is embedded in a domain-independent co-ordination module and it is the rationale, design, implementation and evaluation of this module which forms the major contribution of the paper.

show abstract

Transforming standalone expert systems into a community of cooperating agents

Cited by 48 publications

References 7 publications

Applying Agent Technology

Applying Agent Technology

Constructing a virtual training laboratory using intelligent agents

Designing a reusable co-ordination module for co-operative industrial control applications

Contact Info

Product

Resources

About