A JADE-Based Framework for Developing Evolutionary Multi-Agent Systems
Evolutionary agents are flexible, agile, capable of learning, and appropriate for problems with changing conditions or where the correct solution cannot be known in advance. Evolutionary Multi-Agent systems, therefore, consist of populations of agents that learn through interactions with the environment and with other agents and which are periodically subject to evolutionary processes. In this paper we present a JADE-based programming framework for creating evolutionary multi-agent systems with the aim of providing all the necessary infrastructure for developing multi-agent systems of this type. Through its graphical interface, the framework allows to easily configure the parameters of the multi-agent system, to hold complete control over its execution, and to collect performance data. This way the development of an evolutionary MAS is simplified and only little pieces of code have to be written in order to apply the framework to a particular problem. Along this paper, the features of the framework are described and its capabilities and usage are illustrated through its application to the tic-tac-toe problem.
KeywordsGenetic Algorithm Evolutionary Algorithm Graphical Interface Rule Base MultiAgent System
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