Using Adaptation and Organisational Knowledge to Coordinate Mobile Agents

Quality of Service (QoS) routing generally requires fast reaction times, tight coupling of interactions between routing systems and mechanisms for ensuring that actions taken throughout the network are coherent. [18] showed how an agent based QoS routing approach can benefit significantly from making controller agents mobile and allowing them adapt the information and control distribution in the network over time. This paper discusses how giving mobile agents organisational models can bridge the gap between the need for tight, fast coordination and freedom to move around the network. Furthermore coordination is achieved without imposing any globally or external controls on the mobile agents in the system.

[1]  Nicholas R. Jennings,et al.  Foundations of distributed artificial intelligence , 1996, Sixth-generation computer technology series.

[2]  Janet Bruten,et al.  Ant-like agents for load balancing in telecommunications networks , 1997, AGENTS '97.

[3]  Luca Cardelli,et al.  Mobile Ambients , 1998, FoSSaCS.

[4]  S. Appleby,et al.  Mobile Software Agents for Control in Telecommunications Networks , 2000 .

[5]  Franco Zambonelli,et al.  TuCSoN: a Coordination Model for Mobile Agents , 1998 .

[6]  Michael Wooldridge,et al.  Formalizing the Cooperative Problem Solving Process , 1994 .

[7]  Devika Subramanian,et al.  Ants and Reinforcement Learning: A Case Study in Routing in Dynamic Networks , 1997, IJCAI.

[8]  Franco Zambonelli,et al.  Co-ordination of mobile information agents in TuCSoN , 1998, Internet Res..

[9]  Sebastiano Trigila.  Intelligence in Services and Networks: Technology for Ubiquitous Telecom Services , 1998, Lecture Notes in Computer Science.

[10]  Amy L. Murphy,et al.  LIME: Linda meets mobility , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[11]  Klara Nahrstedt,et al.  An overview of quality of service routing for next-generation high-speed networks: problems and solutions , 1998, IEEE Netw..

[12]  C. V. Ramamoorthy,et al.  An Adaptive Hierarchical Routing Protocol , 1989, IEEE Trans. Computers.

[13]  Nicholas R. Jennings,et al.  Coordination techniques for distributed artificial intelligence , 1996 .

[14]  Nicholas M. Avouris,et al.  Distributed artificial intelligence: theory and praxis , 1992 .

[15]  Steven Willmott,et al.  The benefits of environment adaptive organisations for agent coordination and network routing problems , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[16]  Les Gasser.  DAI approaches to coordination , 1992 .

[17]  Thomas Magedanz,et al.  On the Usage of Standard Mobile Agent Platforms in Telecommunication Environments , 1998, IS&N.

[18]  Les Gasser,et al.  Social Conceptions of Knowledge and Action: DAI Foundations and Open Systems Semantics , 1991, Artif. Intell..

[19]  Boi Faltings,et al.  Abstraction and constraint satisfaction techniques for planning bandwidth allocation , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[20]  Pattie Maes,et al.  Cooperating Mobile Agents for Dynamic Network Routing , 1999 .