Ecology Based Decentralized Agent Management System

The problem of maintaining a desired number of mobile agents on a network is not trivial, especially if we want a completely decentralized solution. Decentralized control makes a system more robust and less susceptible to partial failures. The problem is exacerbated on wireless ad hoc networks where host mobility can result in significant changes in the network size and topology. In this paper we propose an ecology-inspired approach to the management of the number of agents. The approach associates agents with living organisms and tasks with food. Agents procreate or die based on the abundance of uncompleted tasks (food). We performed a series of experiments investigating properties of such systems and analyzed their stability under various conditions. We concluded that the ecology based metaphor can be successfully applied to the management of agent populations on wireless ad hoc networks.

[1]  M. Conrad,et al.  Evolution experiments with an artificial ecosystem. , 1970, Journal of theoretical biology.

[2]  Kristina Lerman,et al.  Macroscopic analysis of adaptive task allocation in robots , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[3]  G. Gilbert.  Book Review of The computational beauty of nature: Computer explorations of fractals, chaos, complex systems and adaptation. Gary William Flake , 2000 .

[4]  Russell P. Lentini,et al.  EMAA : An Extendable Mobile Agent Architecture , 1998 .

[5]  John R. Koza,et al.  Genetic Programming III: Automatic Pro-gramming and Automatic Circuit Synthesis , 2001 .

[6]  M Conrad,et al.  Evolve II: a computer model of an evolving ecosystem. , 1985, Bio Systems.

[7]  Pierre Sens,et al.  Towards Adaptive Fault-Tolerance For Distributed Multi-Agent Systems , 2001 .

[8]  J M Smith,et al.  Evolution and the theory of games , 1976 .

[9]  M. Rizki,et al.  Evolve III: a discrete events model of an evolutionary ecosystem. , 1985, Bio Systems.

[10]  Kristina Lerman,et al.  A General Methodology for Mathematical Analysis of Multi-Agent Systems , 2001 .

[11]  Hector J. Levesque,et al.  The adaptive agent architecture: achieving fault-tolerance using persistent broker teams , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[12]  Felix C. Freiling,et al.  Fundamentals of Fault-Tolerant Distributed Computing in Asynchronous Environments , 1999, ACM Comput. Surv..

[13]  William C. Regli,et al.  Network Meta-Reasoning for Information Assurance in Mobile Agent Systems , 2003, IJCAI.

[14]  J. M. Smith,et al.  The Logic of Animal Conflict , 1973, Nature.

[15]  David R. Jefferson,et al.  RAM: Artificial Life for the Exploration of Complex Biological Systems , 1987, IEEE Symposium on Artificial Life.

[16]  Moshe Kam,et al.  Secure Mobile Agents on Ad Hoc Wireless Networks , 2003, IAAI.

[17]  A. M. Assad,et al.  Emergent colonization in an artificial ecology , 1992 .

[18]  Siome Goldenstein,et al.  Non-linear dynamical system approach to behavior modeling , 1999, The Visual Computer.

[19]  Ronaldo A. Sequeira,et al.  An emergent computational approach to the study of ecosystem dynamics , 1995 .

[20]  F. Varela,et al.  Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life , 1992 .

[21]  Evan Sultanik,et al.  Network awareness for mobile agents on ad hoc networks , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[22]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[23]  Gary William Flake,et al.  The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems and Adaptation , 1998 .

[24]  Sarit Kraus,et al.  Probabilistically Survivable MASs , 2003, IJCAI.