Intelligent Multi-Agent Based Information Management Methods to Direct Complex Industrial Systems
Affiliation(s)
Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy. Department of Computer Science, Sapienza University of Rome, Rome, Italy.
Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy. Department of Computer Science, Sapienza University of Rome, Rome, Italy.
ABSTRACT
In recent years, the increasingly complexity of the logistic and technical aspects of the novel manufacturing environments, as well as the need to increase the performance and safety characteristics of the related cooperation, coordi-nation and control mechanisms is encouraging the development of new information management strategies to direct and man- age the automated systems involved in the manufacturing processes. The Computational Intelligent (CI) approaches seem to provide an effective support to the challenges posed by the next generation industrial systems. In particular, the Intelligent Agents (IAs) and the Multi-Agent Systems (MASs) paradigms seem to provide the best suitable solutions. Autonomy, flexibility and adaptability of the agent-based technology are the key points to manage both automated and information processes of any industrial system. The paper describes the main features of the IAs and MASs and how their technology can be adapted to support the current and next generation advanced industrial systems. Moreover, a study of how a MAS is utilized within a productive process is depicted.
In recent years, the increasingly complexity of the logistic and technical aspects of the novel manufacturing environments, as well as the need to increase the performance and safety characteristics of the related cooperation, coordi-nation and control mechanisms is encouraging the development of new information management strategies to direct and man- age the automated systems involved in the manufacturing processes. The Computational Intelligent (CI) approaches seem to provide an effective support to the challenges posed by the next generation industrial systems. In particular, the Intelligent Agents (IAs) and the Multi-Agent Systems (MASs) paradigms seem to provide the best suitable solutions. Autonomy, flexibility and adaptability of the agent-based technology are the key points to manage both automated and information processes of any industrial system. The paper describes the main features of the IAs and MASs and how their technology can be adapted to support the current and next generation advanced industrial systems. Moreover, a study of how a MAS is utilized within a productive process is depicted.
Cite this paper
D. Avola, L. Cinque and G. Placidi, "Intelligent Multi-Agent Based Information Management Methods to Direct Complex Industrial Systems," Intelligent Information Management, Vol. 4 No. 6, 2012, pp. 338-347. doi: 10.4236/iim.2012.46038.
D. Avola, L. Cinque and G. Placidi, "Intelligent Multi-Agent Based Information Management Methods to Direct Complex Industrial Systems," Intelligent Information Management, Vol. 4 No. 6, 2012, pp. 338-347. doi: 10.4236/iim.2012.46038.
References
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[1] R. Lee, “Computer and Information Science, Studies in Computational Intelligence,” Springer-Verlag, Berlin and Heidelberg, 2012. doi:10.1007/978-3-642-30454-5 [2] L. Benyoucef and B. Grabot, “Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management,” Springer-Verlag, London, 2010. doi:10.1007/978-1-84996-119-6 [3] D. Laha and P. Mandal, “Handbook of Computational Intelligence in Manufacturing and Production Management,” IGI Publishing, Hershey, 2007. doi:10.4018/978-1-59904-582-5 [4] K. Hermann, “Distributed Manufacturing: Paradigm, Concepts, Solutions and Examples,” Springer-Verlag, London, 2010. doi:10.1007/978-1-84882-707-3 [5] W. Xiang and H. P. Lee, “Ant Colony Intelligence in Multi-Agent Dynamic Manufacturing Scheduling,” Journal of Engineering Applications of Artificial Intelligence, Vol. 21, No. 1, 2008, pp. 73-85. doi:10.1016/j.engappai.2007.03.008 [6] D. S. Kim, C. S. Kim and K. W. Rim, “Modeling and Design of Intelligent Agent System,” International Journal of Control, Automation, and Systems, Vol. 1, No. 2, 2003, pp. 257-261. [7] V. Graudina and J. Grundspenkis, “Technologies and Multi-Agent System Architectures for Transportation and Logistics Support: An Overview,” Proceedings of the International Conference on Computer Systems and Technologies (CompSysTech’05), Varna, 16-17 June 2005, pp. IIIA.6-1-IIIA.6-6. [8] S. J. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach,” 3rd Edition, Prentice-Hall Inc., New Jersey, 2010. [9] C. Z. Janikow, “Fuzzy Decision Trees: Issues and Methods,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 28, No. 14, 1998, pp. 1-14. doi:10.1109/3477.658573 [10] P. Stone and M. Veloso, “Using Decision Tree Confidence Factors for Multi-Agent Control,” Proceeding of the Second International Conference on Autonomous Agents (AGENTS’98), Minneapolis, 10-13 May 1998, pp. 86-91. doi:10.1145/280765.280780 [11] D. Mitrovi?, M. Ivanovi? and M. Vidakovi?, “Introducing ALAS: A Novel Agent-Oriented Programming Language,” Proceeding of the International Conference on Numerical Analysis and Applied Mathematics, 2011, pp. 861-864. doi:10.1063/1.3636869 [12] Y. Shoham, “Agent-Oriented Programming,” Journal of Artificial Intelligence, Vol. 60, No. 1, 1993, pp. 51-92. doi:10.1016/0004-3702(93)90034-9 [13] M. D’Inverno, M. Luck, M. Georgeff, D. Kinny and M. Wooldridge, “The dMARS Architecture: A Specifiction of the Distributed Multi-Agent Reasoning System,” Journal of Autonomous Agents and Multi-Agent Systems, Vol. 9, No. 1-2, 2004, pp. 5-53. doi:10.1023/B:AGNT.0000019688.11109.19 [14] M. Winikoff, “JACK Intelligent Agents: An Industrial Strength Platform, Multi-Agent Programming: Languages, Platforms and Applications,” Proceeding of the International Conference in Multiagent Systems, Artificial Societies, and Simulated Organizations, Vol. 15, No. 2, 2005, pp. 175-193. doi:10.1007/0-387-26350-0_7 [15] K. V. Hindriks, F. S. De Boer, W. Van Der Hoek and J.-J. C. Meyer, “Agent Programming in 3APL,” Proceeding of Autonomous Agents and Multi-Agent Systems, Vol. 2, No. 4, 1999, pp. 357-401. doi:10.1023/A:1010084620690 [16] H. Sevay and C. Tsatsoulis, “Multiagent Reactive Plan Application Learning in Dynamic Environments,” Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 2 (AAMAS’02), Bologna, 15-19 July 2002, pp. 839-840. doi:10.1145/544862.544937 [17] N. M. Avouris and L. Gasser, “Distributed Artificial Intelligence: Theory and Praxis,” Kluwer Academic Publishers, Norwell, 1991. [18] S. Kovalchuk, A. Larchenko and A. Boukhanovsky, “Knowledge-Based Resource Management for Distributed Problem Solving,” Proceeding of the International Conference on Knowledge Engineering and Management, Vol. 123, No. 2012, 2012, pp. 121-128. doi:10.1007/978-3-642-25661-5_16 [19] N. Ronald, “Modelling Pedestrian Behaviour Using the BDI Architecture,” Proceeding of the IEEE/WIC/ACM International conference on Intelligent Agent Technology, Compiègne, 19-22 September 2005, pp. 161-164. doi:10.1109/IAT.2005.104 [20] M.-P. Huget, “The Foundation for Intelligent Physical Agents,” 2012. http://www.fipa.org [21] R. J. Trudeau, “Introduction to Graph Theory,” 2nd Edition, Dover Publications, Mineola, New York, 1993. [22] B. Parhami, “Introduction to Parallel Processing: Algorithms and Architecture,” Plenum Press, New York, 1999. [23] T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms,” McGraw-Hill, New York, 2002. [24] T. Mentzer, “Supply Chain Management,” Sage Publications Ltd., Thousand Oaks, 2000. [25] M. Mohammadian, “Designing Unsupervised Hierarchical Fuzzy Logic Systems,” Machine Learning: Concepts, Methodologies, Tools and Applications, IGI Global, Hershey, 2011, pp. 253-261. [26] H. Colestock, “Industrial Robotics,” McGraw-Hill, New York, 2005. [27] D. Patrick and S. Fardo, “Industrial Process Control Systems,” 2nd Edition, The Fairmont Press, Lilburn, 2011. [28] S. Kraus, “Strategic Negotiation in Multiagent Environments, Intelligent Robotics and Autonomous Agents,” The MIT Press, Cambridge, 2001. [29] S. J. Kollmansberger and S. L. Mabry, “Intelligent Agent Generation with the DNA-MAS Genetic Programming System,” Proceeding of the International Conference on Artificial Intelligence and Soft Computing, Banf, 17-19 July 2002, pp. 101-116. [30] L. Monch, S. Marcel and J. Zimmermann, “FABMAS: An Agent-Based System for Production Control of Semiconductor Manufacturing Processes,” Proceeding of the 1st International Conference on Industrial Applications of Holonic and Multi-agent Systems (HoloMAS’03), Prague, 1-3 September 2003, pp. 258-267. doi:10.1007/978-3-540-45185-3_24