Abstract
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.
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References
Di Nola, A., Gisolfi, A., Loia, V., Sessa, S.: Emerging Behaviors in Fuzzy Evolutionary Agents. In: 7th European Congress on Intell. Tech. and Soft Comput., EUFIT 1999 (1999)
FIPA, Foundation for Intelligent Physical Agents (last accessed 30/10/2008) (2008), http://www.fipa.org
Hinton, G.E., Nowlan, S.J.: How Learning Can Guide Evolution. Complex Systems 1, 495–502 (1987)
JADE, Java Agent DEvelopment Framework (last accessed 30/10/2008) (2008), http://jade.tilab.com
Menczer, F., Monge, A.E.: Scalable Web Search by Adaptive Online Agents: An InfoSpiders Case Study. In: Klusch, M. (ed.) Intelligent Information Agents: Agent-Based Information Discovery and Management on the Internet, pp. 323–347 (1999)
Mitchell, M., Forrest, S.: Genetic Algorithms and Artificial Life. Artificial Life 1, 267–289 (1994)
Moukas, A.: Amalthaea: Information Discovery and Filtering Using Multiagent Evolving Ecosystem. Applied Artificial Intelligence 11(5), 437–457 (1997)
Nonas, E., Poulovassilis, A.: Optimisation of active rule agents using a genetic algorithm approach. In: Quirchmayr, G., Bench-Capon, T.J.M., Schweighofer, E. (eds.) DEXA 1998. LNCS, vol. 1460, pp. 332–341. Springer, Heidelberg (1998)
Wan, Y.: A New Paradigm for Developing intelligent Decision-Making Support Systems (i-DMSS): A Case Study on the Development of Comparison-Shopping Agents. In: Gupta, J.N.D., Forgionne, G.A., Mora, M. (eds.) Intelligent Decision-making Support Systems Foundations, Applications and Challenges, pp. 147–165 (2006)
Wooldridge, M.: Introduction to MultiAgent Systems. John Wiley and Sons, New York (2002)
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Guijarro-Berdiñas, B., Alonso-Betanzos, A., López-López, S., Fernández-Lorenzo, S., Alonso-Ríos, D. (2009). A JADE-Based Framework for Developing Evolutionary Multi-Agent Systems. In: Demazeau, Y., Pavón, J., Corchado, J.M., Bajo, J. (eds) 7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2009). Advances in Intelligent and Soft Computing, vol 55. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00487-2_36
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DOI: https://doi.org/10.1007/978-3-642-00487-2_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00486-5
Online ISBN: 978-3-642-00487-2
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