Abstract
In recent years, reaching agreements is an important problem in multi-agent systems (MAS), which require different types of dialogues between agents. Persuasion is one of them and it is traditionally based on first order logic. However, this type of technique is not adequate for solving complex problems. This situation requires developing new models for optimizing an agent persuasion process. This work presents a persuasion model for MAS based in genetic algorithms (GA) for reaching agreements in problem solving. The objective of this work is to optimize the negotiation between agents that solve complex problems. First, it was designed the persuasion model. Then, it was implemented and evaluated in experimental scenarios and it was compared its results against traditional models. The experimental results showed that a GA-oriented persuasion model optimizes the negotiation in MAS by improving execution time, which also eventually will optimize the processes carried out by MAS.
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References
Solari, M.D.L.Á.: Aplicación de Algoritmos Genéticos en un Sistema Multiagente de Planificación en una Industria Manufacturera. In: XXXII Conferencia Latinoamericana de Informática (2006)
Benedettelli, D.: A LEGO Mindstorms experimental setup for multi-agent systems. In: IEEE ASSP Magazine (2009)
Simonin, O.: A cooperative multi-robot architecture for moving a paralyzed robot. Mechatronics 19, 463–470 (2009)
Balducelli, C., Esposito, C.D.: Genetic agents in an EDSS system to optimise resources management and risk objects evacuation. Saf. Sci. 35, 59–73 (2000)
Dipsis, N., Stathis, K.: Ubiquitous agents for ambient ecologies. Pervasive Mob. Comput. 8, 562–574 (2011)
Wooldridge, M.: An Introduction to Multiagent Systems. Wiley (2002)
Aaron, E., Admoni, H.: Action selection and task sequence learning for hybrid dynamical cognitive agents. Rob. Auton. Syst. 58, 1049–1056 (2010)
Fleming, P.: Genetic Algorithms in Engineering Systems. Institution of Engineering and Technology (1997)
Goolberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley (1989)
Jean-Philippe, V.: Genetic algorithm in a multi-agent system. In: IEEE International Joint Symposia on Intelligence and Systems, pp. 17–26 (1998)
Wooldridge, M., Jennings, N., Kinny, D.: The Gaia methodology for agent-oriented analysis and design. Auton. Agents Multiagents Syst. 3, 285–312 (2000)
Fernando, L., Ossa, C., Car, C.R., Muñoz, G.M., Álvarez, J.A.: Análisis, diseño e implementación de un agente deliberativo para extraer contextos definitorios en textos especializados * María Mer re Resumen Intr oducción Introducción El trabajo interdisciplinario e inter-grupal ha cobrado importancia en campos de, pp. 59–84
Ruiz, R.E.S.: Algoritmo genético para la solución del problema de optimización combinatoria y decisión secuencial en el juego ‘but who’s counting’. Rev. Generación Digit. 9, 65–70 (2011)
Charalambous, C., Hindi, K.S.: Applying GAs to complex problems: the case of scheduling Multi-State intermittent manufacturing systems. In Genetic algorithms in Engineering Systems: Innovations and Applications (1997)
Ying, W., Bin, L.: Job-shop scheduling using genetic algorithm (1996)
Thomas, J., Leon, S.: The ROADMAP meta-model for intelligent adaptive multi-agent system in open environments (2003)
Estévez, P.: Optimizacion mediente algoritmos genéticos. In: Anales del Instituto de Ingenieros de Chile, vol. 1, pp. 83–92 (1997)
Gómez, P., Andres, C., Lario, F.-C.: An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan. Expert Syst. Appl. 39, 8095–8107 (2012)
Obesity and the Economics of Prevention: Fit not Fat: Obesity Update 2014 presents an update of analyses of trends and social disparities in obesity originally presented in OECD report (2014). http://www.oecd.org/els/health-systems/obesity-update.htm
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Jiménez, S., Castillo, V.H., Márquez, B.Y., Alanis, A., Soriano-Equigua, L., Álvarez-Flores, J.L. (2020). A Genetic Algorithm-Oriented Model of Agent Persuasion for Multi-agent System Negotiation. In: Jezic, G., Chen-Burger, J., Kusek, M., Sperka, R., Howlett, R., Jain, L. (eds) Agents and Multi-Agent Systems: Technologies and Applications 2020. Smart Innovation, Systems and Technologies, vol 186. Springer, Singapore. https://doi.org/10.1007/978-981-15-5764-4_26
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DOI: https://doi.org/10.1007/978-981-15-5764-4_26
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