Co-ordination in multi-agent systems

Nwana, Hyacinth S.; Lee, Lyndon C.; Jennings, Nicholas R.

doi:10.1007/3-540-62560-7_37

Cited by 67 publications

(69 citation statements)

References 9 publications

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“…Therefore, the cooperation occurs when an agent is not able to realize a certain task (Marietto, 2000). In Jennings (1995) and Nwana et al (1997) a few reasons related to the coordination between multiple agents are described:…”

Section: Coordinationmentioning

confidence: 99%

Principles of Agent-Oriented Programming

Batista¹,

Marietto²,

Botelho³

et al. 2011

Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications

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Section: Coordinationmentioning

confidence: 99%

Principles of Agent-Oriented Programming

Batista¹,

Marietto²,

Botelho³

et al. 2011

Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications

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“…Considering the complexity inherent to the manufacturing systems, dynamic scheduling is considered an excellent candidate for the application of agent-based technology. In many implementations of MAS systems for manufacturing scheduling, the agents model the resources of the system and the tasks scheduling is done in a distributed way by means of cooperation and coordination amongst agents (Lu & Yih, 2001) (Nwana et al, 1996) (Madureira et al, 2007). When responding to disturbances, the distributed nature of multiagent systems can also be a benefit to the rescheduling algorithm by involving only the agents directly affected, without disturbing the rest of the community that can continue with their work.…”

Section: Related Workmentioning

confidence: 99%

MASDScheGATS - Scheduling System for Dynamic Manufacturing Environmemts

Madureira¹,

Santos²,

Pereira³

2009

Multiagent Systems

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This chapter addresses the resolution of scheduling in manufacturing systems subject to perturbations. The planning of Manufacturing Systems involves frequently the resolution of a huge amount and variety of combinatorial optimisation problems with an important impact on the performance of manufacturing organisations. Examples of those problems are the sequencing and scheduling problems in manufacturing management, routing and transportation, layout design and timetabling problems. New organizational and technological paradigms are needed to reply to the modern manufacturing systems challenges. The traditional structure of manufacturing industries is constructed upon the three pillars of land, labour and capital. The challenge is to move towards a new structure, which can be described as innovating manufacturing, founded on knowledge and capital. Future manufacturing solutions must identify multiple perspectives and linkages between novel approaches to customization, customer response, logistics and maintenance. The current typically linear approach to research, development, design, construction and assembly will be replaced by simultaneous activity in all areas to satisfying global demand and shorten time-to-market (MANUFUTURE, 2004). Multi-agent paradigm is emerging for the development of solutions to very hard distributed computational problems. This paradigm is based either on the activity of "intelligent" agents which perform complex functionalities or on the exploitation of a large number of simple agents that can produce an overall intelligent behaviour leading to the solution of alleged almost intractable problems. The multi-agent paradigm is often inspired by biological systems. Meta-Heuristics (MH) form a class of powerful and practical solution techniques for tackling complex, large-scale combinatorial problems producing efficiently high-quality solutions. From the literature we can conclude that they are adequate for static problems. However, real scheduling problems are quite dynamic, considering the arrival of new orders, orders being cancelled, machine delays or faults, etc. Scheduling problem in dynamic environments have been investigated by a number of authors, see for example (Aytug et al.,

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“…These autonomous components are often referred to as agents. Nwana et al [35] described four co-ordination techniques: organizational structuring, contracting, planning, and negotiation. MacChiaroli and Riemma [36] extended the negotiation process, which is iterative in nature and forces a convergence between demands and offers.…”

Section: Shop Floor Controlmentioning

confidence: 99%

A data mining integrated architecture for shop floor control

Srinivas

Harding

2008

Proceedings of the Institution of Mechanical Engineers, Part B:

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The dynamics of the market are forcing shop floor control systems to enhance their intelligence and reactivity to change. The control system must also have mechanisms to process the vast amount of data, which is generated with the application of cheap data logging systems and should also incorporate a mechanism for continuous learning. Data mining provides a mechanism for analysing data and generating new knowledge. This paper presents an intelligent knowledge-based decision support system for shop floor control, which incorporates data mining techniques and intelligent agent technology to provide useful information, knowledge, and a mechanism for continuous learning. The functioning of the system is described and the architecture of different agents that constitute the system has been discussed in terms of the different modules required. A process to integrate the individual data mining agents associated with the different processes has also been described. Finally a prototype of the system is presented to demonstrate the application of this agent-based system.

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Co-ordination in multi-agent systems

Cited by 67 publications

References 9 publications

Principles of Agent-Oriented Programming

Principles of Agent-Oriented Programming

MASDScheGATS - Scheduling System for Dynamic Manufacturing Environmemts

A data mining integrated architecture for shop floor control

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