Bio-IR-M: A Multi-Paradigm Modelling for Bio-Inspired Multi-Agent Systems

Djamel Zeghida, Djamel Meslati, Nora Bounour

Abstract


Nowadays bio-inspired approaches are widely used. Some of them became paradigms in many domains, such as Ant Colony Optimization (ACO) and Genetic Algorithms (GA). Despite the inherent challenges of surviving, in the natural world, biological organisms evolve, self-organize and self-repair with only local knowledge and without any centralized control. The analogy between biological systems and Multi-Agent Systems (MAS) is more than evident. In fact, every entity in real and natural systems is easily identified as an agent. Therefore, it will be more efficient to model them with agents. In a simulation context, MAS has been used to mimic behavioural, functional or structural features of biological systems. In a general context, bio-inspired systems are carried out with ad hoc design models or with a one target feature MAS model. Consequently, these works suffer from two weaknesses. The first is the use of dedicated models for   restrictive purposes (such as academic projects). The second one is the lack of a design model.

    In this paper, our contribution aims to propose a generic multi-paradigms model for bio-inspired systems. This model is agent-based and will integrate different bio-inspired paradigms with respect of their concepts. We investigate to which extent is it possible to preserve the main characteristics of both natural and artificial systems. Therefore, we introduce the influence/reaction principle to deal with these bio-inspired multi-agent systems.


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Agha, G. A., (1985). Actors: A model of concurrent computation in distributed systems, technical report, DTIC Document.

Amine, L. M. and Nadjet, K., (2015). A Multi-objective Binary Bat Algorithm, Proceedings of the International Conference on Intelligent

Information Processing, Security and Advanced Communication, ACM, 75.

Bakhouya, M., Gaber, J. and Koukam, A., (2003). Bio-inspired model for behavior emergence: Modelling and case study, Procs. Of Knowledge Grid and Grid Intelligence workshop (KGGI’03) at IEEE WI/IAT’03.

Bari, A., Wazed, S., Jaekel, A. and Bandyopad- hyay, S., (2009). A genetic algorithm based approach for energy efficient routing in two- tiered sensor networks, Ad Hoc Networks Journal, Elsevier, (7), (4), 665–676.

Bellifemine, F., Poggi, A. and Rimassa, G., (1999). JADE–A FIPA- compliant agent framework, Proceedings of PAAM, London, (99), 97–108.

Bernon, C., Gleizes, M-P., Peyruqueou, S. and Picard, G., (2002). ADELFE: a methodology for adaptive multi-agent systems engineering, International Workshop on Engineering Societies in the Agents World, Springer, 156–169.

Broecker, B., Caliskanelli, I., Tuyls, K., Sklar, E. and Hennes, D., (2015). Social insect-inspired multi-robot coverage, Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 1775–1776.

Brun, Y., (2008). Building Biologically- Inspired Self-Adapting Systems, Dagstuhl Seminar Proceedings, Schloss Dagstuhl- Leibniz-Zentrum fr Informatik.

Capera, D., George, J.P., Gleizes, M.P. and Glize, P., (2003). The AMAS theory for complex problem solving based on self-organizing cooperative agents, Enabling Technologies: Infrastructure for Collaborative Enterprises, 2003. WET ICE 2003. Proceedings. Twelfth IEEE International Workshops on, IEEE, 383–388.

Colorni, A., Dorigo, M., Maniezzo, V. and others, (1991). Distributed optimization by ant colonies, Proceedings of the first European conference on artificial life, (142), 134–142.

Cuperlier, N., Guedjou, H., de Melo, F., and Miramond, B., (2016). Attention-based smart-camera for spatial cognition, Proceedings of the 10th International Conference on Distributed Smart Camera, ACM, 121–127.

Da Silva, J.L.T. and Demazeau, Y., (2002). Vowels co-ordination model, Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3, ACM, 1129-1136.

Dorigo, M., Maniezzo, V. and Colorni, A., (1991). The ant system: An autocatalytic optimizing process. Tech. rep., Italy: Dipartimento di Elettronica, Politecnico di Milano.

Dorigo, M. and Gambardella, L., (1997). Ant colonies for the travelling salesman problem. BioSystems, Elsevier, 43(2), 73–81.

Dorigo, M. and Di Caro, G., (1999). Ant colony optimization: a new meta-heuristic, Evolutionary Computation, CEC 99. Proceedings of the 1999 Congress on, IEEE, (2), 1470–1477.

Dréo, J., Pétrowski, A., Siarry, P. and Taillard, E., (2006). Metaheuristics for hard optimization: methods and case studies, Springer Science & Business Media.

Ferber, J., and Müller, J-P., (1996). Influences and reaction: a model of situated multiagent systems, Proceedings of Second International Conference on Multi-Agent Systems (ICMAS-96), 72–79.

Ferber, J. and Gutknecht, O., (1998). A meta-model for the analysis and design of organizations in multi-agent systems, MultiAgent Systems, Proceedings. International Conference on, IEEE, 128–135.

Ferber, Jacques, (1999) Multi-agent systems: an introduction to distributed artificial intelligence, Addison-Wesley Reading.

Ferber, J., Michel, F. and Báez, J., (2004). AGRE: Integrating environments with organizations, International Workshop on Environments for Multi-Agent Systems, Springer, 48–56.

Fink, G.A., Haack, J.N., McKinnon, A.D. and Fulp, E.W., (2014). Defense on the move: ant-based cyber defense, IEEE Security & Privacy, IEEE, (12), (2), 36–43.

Florin Leon, Marcin Paprzycki, and Maria Ganzha. (2015). A Review of Agent Platforms, Multi-Paradigm Modelling for Cyber-Physical Systems (MPM4CPS), ICT COST Action IC1404, 1–15.

Fogel, D.B., (1988). An evolutionary approach to the traveling salesman problem, Biological Cybernetics Journal, Springer, (60), (2), 139–144.

Gengan, D., Schoeman, M.A. and Van Der Poll, J.A., (2014). An Ant-based Mobile Agent Approach to Resource Discovery in Grid Computing, Proceedings of the Southern African Institute for Computer Scientist and Information Technologists Annual Conference 2014 on SAICSIT 2014 Empowered by Technology, ACM, 1.

Gonçalves, F. A.C.A., Guimarães, F.G. and Souza, Marcone J.F., (2013). An evolutionary multi-agent system for database query optimization, Proceedings of the 15th annual conference on Genetic and evolutionary computation, ACM, 535–542.

Gutknecht, O. and Ferber, J., (2000). The madkit agent platform architecture, Workshop on Infrastructure for Scalable Multi-Agent Systems at the International Conference on Autonomous Agents, Springer, 48–55.

Hong, T-P., Huang, L-I. and Lin, W-Y., (2014). A Different Perspective on Parallel Sub-Ant-Colonies, Proceedings of the 12th International Conference on Advances in Mobile Computing and Multimedia, ACM, 322–325.

Huget, M-P., (2014). Agent Communication, Agent-Oriented Software Engineering, Springer, 101–133.

Jennings, N.R., Sycara, K. and Wooldridge, M., (1998). A roadmap of agent research and development, Autonomous agents and multi-agent systems, Kluwer Academic Publishers, (1), (1), 7–38.

Jennings, N.R., (2001). An agent-based approach for building complex software systems, Communications ACM Journal, ACM, (44), (4), 35–41.

Karmani, R.K., and Shali, A. and Agha, G., (2009). Actor frameworks for the JVM platform: a comparative analysis, Proceedings of the 7th International Conference on Principles and Practice of Programming in Java, ACM, 11–20.

Karmani, R.K. and Agha, G., (2011). Actors, Encyclopedia of Parallel Computing, Springer, 1–11.

Kosakaya, J., (2016). Multi-agent-based SCADA system, Event-based Control, Communication, and Signal Processing (EBCCSP), 2016 Second International Conference on, IEEE, 1–5.

Lee, U., Magistretti, E., Gerla, M., Bellavista, P., Li´o, P. and Lee, K.W., (2009). Bio-inspired multi-agent data harvesting in a proactive urban monitoring environment, Ad Hoc Networks Journal, Elsevier, (7), (4), 725–741.

Lin, C-T. and Lee, C.S.G., (1991). Neural-network-based fuzzy logic control and decision system, IEEE Transactions on computers, IEEE, (40), (12), 1320–1336.

Lodding, K.N., (2004). The Hitchhiker’s Guide to Biomorphic Software, ACM Queue Journal, ACM, (2), (4), 66–75.

Ma, J., Man, K.L., Ting, T. Zhang, N., Guan, S-U. and Wong, P.WH., (2014). Accelerating Parameter Estimation for Photovoltaic Models via Parallel Particle Swarm Optimization, Computer, Consumer and Control (IS3C), International Symposium on, IEEE, 175–178.

Manate, B., Fortis, F. and Moore, P., (2014). Applying the Prometheus methodology for an Internet of Things architecture, Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, IEEE Computer Society, 435–442.

Mariani, S. and Omicini, A., (2016). Multi-paradigm Coordination for MAS: Integrating Heterogeneous Coordination Approaches in MAS Technologies, WOA, 91–99.

Massawe, L.V., Aghdasi, F. and Kinyua, J., (2009). The development of a multi-agent based middleware for RFID asset management system using the PASSI methodology, Information Technology: New Generations, ITNG’09. Sixth International Conference on, IEEE, 1042–1048.

Michel, F., (2004). Formalism, tools and methodological elements for the modeling and simulation of multi-agents systems, PhD thesis, LIRMM, Montpellier, France.

Michel, F., Ferber, J., Drogoul, A. and others, (2009). Multi-Agent Systems and Simulation: a Survey From the Agents Community’s Perspective, Multi-Agent Systems: Simulation and Applications Journal, 3–52.

Mirjalili, S., Mirjalili, S.M. and Lewis, A., (2014). Grey wolf optimizer, Advances in engineering software, Elsevier, (69), 46–61.

Mochalov, V., (2015). Multi-agent bio-inspired algorithms for wireless sensor network design, Advanced Communication Technology (ICACT), 17th International Conference on, IEEE, 33–42.

Nagpal, R., (2003). A catalog of biologically-inspired primitives for engineering self-organization, International Workshop on Engineering Self-Organising Applications, Springer, 53–62.

Olaifa, M., Mapayi, T. and Van Der Merwe, R., (2015). Multi Ant LA: An adaptive multi agent resource discovery for peer to peer grid systems, Science and Information Conference (SAI), IEEE, 447–451.

Padmanaban, R., Thirumaran, M., Suganya, K. and Priya, R.V., (2016). AOSE Methodologies and Comparison of Object Oriented and Agent Oriented Software Testing, Proceedings of the International Conference on Informatics and Analytics, ACM, 119.

Parunak, H.V.D., (1997). ”Go to the ant”: Engineering principles from natural multi-agent systems, Annals of Operations Research Journal, JC BALTZER AG, (75), 69–102.

Perez-Carabaza, S., Besada-Portas, E., Lopez-Orozco, J.A. and de la Cruz, J.M., (2016). A Real World Multi-UAV Evolutionary Planner for Minimum Time Target Detection, Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, ACM, 981–988.

Perez-Diaz, F., Zillmer, R. and Groß, R., (2015). Firefly-Inspired Synchronization in Swarms of Mobile Agents, Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 279–286.

Qian, B. and Cheng, H.H., (2016). A mobile agent-based coalition formation system for multi-robot systems, Mechatronic and Embedded Systems and Applications (MESA), 2016 12th IEEE/ASME International Conference on, IEEE, 1–6.

Rehberger, S., Spreiter, L. and Vogel-Heuser, B., (2016). An agent approach to flexible automated production systems based on discrete and continuous reasoning, Automation Science and Engineering (CASE), IEEE International Conference on, IEEE, 1249–1256.

Rowley, H.A., Baluja, S. and Kanade, T., (1998). Neural network-based face detection, Pattern Analysis and Machine Intelligence Journal, IEEE Transactions on, IEEE, (20), (1), 23–38.

Silva, D.C., Braga, R.A-M. and Reis, L.P., and Oliveira, E., (2010). A generic model for a robotic agent system using GAIA methodology: Two distinct implementations, Robotics Automation and Mechatronics (RAM), IEEE Conference on, IEEE, 280–285.

Sturm, A. and Shehory, O., (2014). Agent-oriented software engineering: revisiting the state of the art, Agent-Oriented Software Engineering, Springer, 13–26.

Sturm, A. and Shehory, O., (2014). The landscape of agent-oriented methodologies, Agent-Oriented Software Engineering, Springer, 137–154.

Stützle, T. and Hoos, H., (1998). Improvements on the ant-system: Introducing the max-min ant system. Artificial Neural Nets and Genetic Algorithms, Springer, 245–249.

Tarakanov, A., (2001). Information security with formal immune networks, Information Assurance in Computer Networks Journal, LNCS, Springer, (2052), 115–126.

Tsang, C.H. and Kwong, S., (2005). Multi-agent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction, Industrial Technology, ICIT 2005. IEEE International Conference on, IEEE, 51-56.

Wang, J., Cao, J., Li, B., Lee, S. and Sherratt, R. S., (2015). Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks, IEEE Transactions on Consumer Electronics, IEEE, (61), (4), 438–444.

Weyns, D., Parunak, H.V.D. and Michel, F., (2006). Environments for Multi-Agent Systems II, Second International Workshop, E4MAS 2005, Utrecht, The Netherlands, July 25, 2005, Selected Revised and Invited Papers, Springer, (3830).

Wooldridge, M. and Jennings, N.R., (1994). Agent theories, architectures, and languages: a survey, International Workshop on Agent Theories, Architectures, and Languages, Springer , 1–39.

Xiang, W. and Lee, HP., (2008). Ant colony intelligence in multi-agent dynamic manufacturing scheduling, Engineering Applications of Artificial Intelligence Journal, Elsevier, (21), (1), 73–85.

Zambonelli, F., (2015). Engineering Environment-Mediated Coordination via Nature-Inspired Laws, Agent Environments for Multi-Agent Systems IV, Springer, 63–75.

Zambonelli, F., Omicini, A., Anzengruber, B., Castelli, G., De Angelis, F.L., Serugendo, G.Di M., and Dobson, S., Fernandez-Marquez, J.L., Ferscha, A., Mamei, M. and others, (2015). Developing pervasive multi-agent systems with nature-inspired coordination, Pervasive and Mobile Computing, Elsevier, (17), 236–252.

Zeghida, D., (2003). MALCS: Multi-Agent Learning Companions System, DEA thesis, Badji Mokhtar Annaba University, Algeria.

Zeghida, D., Meslati, D., Bounour, N., Michel, F., and Allat Y., (2018). Agent Influence / Reaction Ant System Variants: An Experimental Comparison, International Journal of Artificial Intelligence, Accepted.




DOI: https://doi.org/10.31449/inf.v42i3.1516

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