Optimisation of active rule agents using a genetic algorithm approach
Intelligent agents and active databases have a number of common characteristics, the most important of which is that they both execute actions by firing rules upon events occurring provided certain conditions hold. This paper assumes that the knowledge of an intelligent agent is expressed using a set of active rules and proposes a method for optimising the rule-base of such an agent using a Genetic Algorithm. We illustrate the applicability of this method by using it to optimise the performance of a self-adaptive network. The benefits of our approach are simplified design and reduced development and maintenance times of rule-based agents in the face of dynamically evolving environments.
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- 1.J. Bailey, M. Georgeff, D. B. Kemp, and D. Kinny, “Active databases and agent systems — A comparison”, Lecture Notes in Computer Science, 985, 342–356, (1995).Google Scholar
- 2.Michael Bratman, Intention, plans, and practical reason, Harvard University press, 1987.Google Scholar
- 3.Lawrence Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, 1991.Google Scholar
- 4.K. R. Dittrich, S. Gatziu, and A. Geppert, “The active database management system manifesto: A rulebase of ADBMS features”, Lecture Notes in Computer Science, 985, 3–17, (1995).Google Scholar
- 5.Klaus Fischer, Jorg P. Muller, and Markus Pischel, “A pragmatic BDI architecture”, in Proceedings on the IJCAI Workshop on Intelligent Agents II: Agent Theories, Architectures, and Languages, volume 1037 of LNAI, pp. 203–218, Berlin, (19–20 August 1996). Springer Verlag.Google Scholar
- 6.D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Mass., 1989.Google Scholar
- 7.David Goldberg, Genetic Algorithms, Addison Wesley, Reading, 1989.Google Scholar
- 8.Thomas Haynes and Sandip Sen, “Evolving behavioral strategies in predators and prey”, in IJCAI-95 Workshop on Adaptation and Learning in Multiagent Systems, pp. 32–37, (1995).Google Scholar
- 9.David Kinny, Michael Georgeff, and Anand Rao, “A methodology and modelling technique for systems of BDI agents”, in Proceedings of the 7th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, volume 1038 of LNAI, pp. 56–71, Berlin, (22–25 January 1996). Springer Verlag.Google Scholar
- 10.Mauro Manela and J. A. Campbell, “Designing good pursuit problems as testbeds for distributed AI: A novel application of genetic algorithms, in Proceedings of the 5th European Workshop on Modelling Autonomous Agents in a Multi-Agend World (MAAMAW'93), volume 957 of LNAI, pp. 231–252, Berlin, GER, (August 1995). Springer.Google Scholar
- 11.Anand S. Rao and Michael P. Georgeff, “BDI agents: from theory to practice”, in Proceedings of the First International Conference on Multi—Agent Systems, pp. 312–319, San Francisco, CA, (1995). MIT Press.Google Scholar
- 12.Johan van den Akker and Arno Siebes, “Enriching active databases with agent technology”, in Proceedings ot the First International Workshop on Cooperative Information Agents, volume 1202 of LNAI, pp. 116–125, Berlin, (February26–28 1997). Springer.Google Scholar