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Towards adaptive multi-robot systems: self-organization and self-adaptation

Published online by Cambridge University Press:  04 October 2018

Christopher-Eyk Hrabia ,
Marco Lützenberger  and
Sahin Albayrak
Christopher-Eyk Hrabia
Affiliation:
DAI-Labor, Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Ernst-Reuter-Platz 7, 10587 Berlin, Germany; e-mail: christopher-eyk.hrabia@dai-labor.de, marco.luetzenberger@dai-labor.de, Sahin.Albayrak@dai-labor.de
Marco Lützenberger
Affiliation:
DAI-Labor, Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Ernst-Reuter-Platz 7, 10587 Berlin, Germany; e-mail: christopher-eyk.hrabia@dai-labor.de, marco.luetzenberger@dai-labor.de, Sahin.Albayrak@dai-labor.de
Sahin Albayrak
Affiliation:
DAI-Labor, Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Ernst-Reuter-Platz 7, 10587 Berlin, Germany; e-mail: christopher-eyk.hrabia@dai-labor.de, marco.luetzenberger@dai-labor.de, Sahin.Albayrak@dai-labor.de
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Abstract

The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible.

Type
Principles and Practice of Multi-Agent Systems
Information
The Knowledge Engineering Review , Volume 33 , 2018 , e16
Copyright
© Cambridge University Press, 2018 

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References

Ali, S. M., Zimmer, R. M., Elstob, C. M. & Dubois, D. M. 1998. The question concerning emergence: implications for artificiality. AIP Conference Proceedings 437(1), 138–156.Google Scholar
Allgeuer, P. & Behnke, S. 2013. Hierarchical and state-based architectures for robot behavior planning and control. In Proceedings of 8th Workshop on Humanoid Soccer Robots, IEEE-RAS International Conference on Humanoid Robots.Google Scholar
Ashley-Rollman, M., Goldstein, S., Lee, P., Mowry, T. & Pillai, P. 2007. Meld: a declarative approach to programming ensembles. In IEEE/RSJ International Conference on Intelligent Robots and Systems,IROS, 2794–2800.Google Scholar
Ay, N., Der, R. & Prokopenko, M. 2012. Guided self-organization: perception-action loops of embodied systems. Theory in Biosciences 131(3), 125–127.Google Scholar
Bachrach, J., McLurkin, J. & Grue, A. 2008. Protoswarm: A language for programming multi-robot systems using the amorphous medium abstraction. In Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3, 1175–1178. AAMAS ’08. International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
Balch, T. & Arkin, R. C. 1998. Behavior-based formation control for multirobot teams. IEEE Transactions on Robotics and Automation 14(6), 926–939.Google Scholar
Bashyal, S. & Venayagamoorthy, G. 2008. Human swarm interaction for radiation source search and localization. In IEEE Swarm Intelligence Symposium, 2008, 1–8. SIS 2008.Google Scholar
Beal, J. & Bachrach, J. 2006. Infrastructure for engineered emergence on sensor/actuator networks. IEEE Intelligent Systems 21(2), 10–19.Google Scholar
Blum, A. L. & Furst, M. L. 1997. Fast planning through planning graph analysis. Artificial Intelligence 90(1–2), 281–300.Google Scholar
Bohren, J. & Cousins, S. 2010. The SMACH high-level executive [ros news]. Robotics Automation Magazine, IEEE 17(4), 18–20.Google Scholar
Bonabeau, E., Dorigo, M. & Theraulaz, G. 1999. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press Inc.Google Scholar
Bonet, B. & Geffner, H. 2001. Heuristic search planner 2.0. AI Magazine 22(3), 77.Google Scholar
Bonjean, N., Mefteh, W., Gleizes, M. P., Maurel, C. & Migeon, F. 2014. Adelfe 2.0. In Handbook on Agent-Oriented Design Processes, Cossentino, M., Hilaire, V., Molesini, A. & Seidita, V. (eds). Springer, 19–63.Google Scholar
Brambilla, M., Ferrante, E., Birattari, M. & Dorigo, M. 2013. Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence 7(1), 1–41.Google Scholar
Brambilla, M., Pinciroli, C., Birattari, M. & Dorigo, M. 2012. Property-driven design for swarm robotics. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 1, 139–146.Google Scholar
Branke, J., Mnif, M., Muller-Schloer, C. & Prothmann, H. 2006. Organic computing – addressing complexity by controlled self-organization. In 2th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation ISoLA, 185–191. IEEE.Google Scholar
Bresciani, P., Perini, A., Giorgini, P., Giunchiglia, F. & Mylopoulos, J. 2004. Tropos: an agent-oriented software development methodology. Autonomous Agents and Multi-Agent Systems 8(3), 203–236.Google Scholar
Breuer, T., Giorgana Macedo, G. R., Hartanto, R., Hochgeschwender, N., Holz, D., Hegger, F., Jin, Z., Müller, C., Paulus, J., Reckhaus, M., Álvarez Ruiz, J. A., Plöger, P. G. & Kraetzschmar, G. K. 2012. Johnny: an autonomous service robot for domestic environments. Journal of Intelligent & Robotic Systems 66(1), 245–272.Google Scholar
Brooks, R. A. 1986. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation 2(1), 14–23.Google Scholar
Brun, Y., Serugendo, G. D. M., Gacek, C., Giese, H., Kienle, H., Litoiu, M., Müller, H, Pezzè, M. & Shaw, M. 2009. Engineering self-adaptive systems through feedback loops. In Software Engineering for Self-Adaptive Systems, 48–70. Springer.Google Scholar
Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G. & Bonabeau, E. 2003. Self-Organization in Biological Systems. Princeton University Press.Google Scholar
CogniTeam Ltd 2015. Cognitao (think as one). http://www.cogniteam.com/cognitao.html.Google Scholar
Cui, Y., Voyles, R., Lane, J. & Mahoor, M. 2014. ReFrESH: a self-adaptation framework to support fault tolerance in field mobile robots. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1576–1582.Google Scholar
Das, R., Crutchfield, J. P., Mitchell, M. & Hanson, James E. 1995. Evolving globally synchronized cellular automata. In Proceedings of the 6th International Conference on Genetic Algorithms, 336–343. Morgan Kaufmann Publishers Inc.Google Scholar
De Nicola, R., Ferrari, G., Loreti, M. & Pugliese, R. 2013. A language-based approach to autonomic computing. In Formal Methods for Components and Objects, Beckert, B., Damiani, F., de Boer, F. & Bonsangue, M., (eds), Lecture Notes in Computer Science, 7542 . Springer, 25–48.Google Scholar
De Rosa, M., Goldstein, S., Lee, P., Pillai, P. & Campbell, J. 2008. Programming modular robots with locally distributed predicates. In IEEE International Conference on Robotics and Automation (ICRA), 3156–3162.Google Scholar
De Wolf, T. & Holvoet, T. 2005. Emergence versus self-organisation: different concepts but promising when combined. In Engineering Self-Organising Systems, Brueckner, S. A., Marzo Serugendo, G., Karageorgos, A. & Nagpal, R. (eds). Springer-Verlag, 1–15.Google Scholar
De Wolf, T. & Holvoet, T. 2007. Design patterns for decentralised coordination in self-organising emergent systems. In Engineering Self-Organising Systems, 28–49. Springer.Google Scholar
De Wolf, T., Samaey, G. & Holvoet, T. 2005. Engineering self-organising emergent systems with simulation-based scientific analysis. In 4th International Workshop on Engineering Self-Organising Applications, 146–160.Google Scholar
Deneubourg, J. L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C. & Chrétien, L. 1990. The dynamics of collective sorting robot-like ants and ant-like robots. In Proceedings of the First International Conference on Simulation of Adaptive Behavior on From Animals to Animats, 356–363. MIT Press.Google Scholar
Desai, J. P., Ostrowski, J. P. & Kumar, V. 2001. Modeling and control of formations of nonholonomic mobile robots. IEEE Transactions on Robotics and Automation 17(6), 905–908.Google Scholar
Dorigo, M., Floreano, D., Gambardella, L., Mondada, F., Nolfi, S., Baaboura, T., Birattari, M., Bonani, M., Brambilla, M., Brutschy, A., Burnier, D., Campo, A., Christensen, A., Decugniere, A., Di Caro, G., Ducatelle, F., Ferrante, E., Forster, A., Martinez Gonzales, J., Guzzi, J., Longchamp, V., Magnenat, S., Mathews, N., Montes de Oca, M, O’Grady, R., Pinciroli, C., Pini, G., Retornaz, P., Roberts, J., Sperati, V., Stirling, T., Stranieri, A., Stutzle, T., Trianni, V., Tuci, E., Turgut, A. & Vaussard, F. 2013. Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robotics Automation Magazine 20(4), 60–71.Google Scholar
Dorigo, M., Trianni, V., Şahin, E., Groβ, R., Labella, T. H, Baldassarre, G., Nolfi, S., Deneubourg, J.-L., Mondada, F., Floreano, D. & Gambardella, L. M. 2004. Evolving self-organizing behaviors for a swarm-bot. Autonomous Robots 17(2–3), 223–245.Google Scholar
Ducatelle, F., Di Caro, G. A. & Gambardella, L. M. 2010. Cooperative self-organization in a heterogeneous swarm robotic system. In 12th Annual Conference on Genetic and Evolutionary Computation, GECCO’10, 87–94. ACM.Google Scholar
Edmonds, B. 2005. Using the experimental method to produce reliable self-organised systems. In Brueckner, S. A, Di Marzo Serugendo, G., Karageorgos, A. & Nagpal, R. (eds) Engineering Self-Organising Systems: Methodologies and Applications. Springer, 84–99.Google Scholar
Edmonds, B. & Bryson, J. J. 2004. The insufficiency of formal design methods “ The Necessity of an Experimental Approach - for the Understanding and Control of Complex MAS”. In 3th International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2, AAMAS ’04, 938–945. IEEE Computer Society.Google Scholar
Fernandez-Marquez, J. L., Serugendo, G. D. M. & Montagna, S. 2011. Bio-core: bio-inspired self-organising mechanisms core. In International Conference on Bio-Inspired Models of Network, Information, and Computing Systems, 59–72. Springer.Google Scholar
Fernandez-Marquez, J. L., Serugendo, G. D. M., Montagna, S., Viroli, M. & Arcos, J. L. 2012. Description and composition of bio-inspired design patterns: a complete overview. Natural Computing 12(1), 43–67.Google Scholar
Fikes, R. E. & Nilsson, N. J. 1972. STRIPS: a new approach to the application of theorem proving to problem solving. Artificial Intelligence 2(3), 189–208.Google Scholar
Francesca, G., Brambilla, M., Brutschy, A., Garattoni, L., Miletitch, R., Podevijn, G., Reina, A., Soleymani, T., Salvaro, M., Pinciroli, C., Mascia, F., Trianni, V. & Birattari, M. 2015. AutoMoDe-Chocolate: automatic design of control software for robot swarms. Swarm Intelligence 9(2–3), 125–152.Google Scholar
Garlan, D., Cheng, S.-W., Huang, A.-C., Schmerl, B. & Steenkiste, P. 2004. Rainbow: architecture-based self-adaptation with reusable infrastructure. Computer 37(10), 46–54.Google Scholar
Gershenson, C. 2007. Design and control of self-organizing systems. PhD thesis, Vrije Universiteit Brussel.Google Scholar
Goebel, P. 2013. ROS By Example. Lulu.Google Scholar
Goldstein, J. 1999. Emergence as a construct: history and issues. Emergence 1(1), 49–72.Google Scholar
Goodrich, M. A., Pendleton, B., Sujit, P. B. & Pinto, J. 2011. Toward human interaction with bio-inspired robot teams. In 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2859–2864. IEEE.Google Scholar
Graff, D., Richling, J. & Werner, M. 2013. Programming and managing the swarm - an operating system for an emerging system of mobile devices. In IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, 0, 9–16. IEEE Computer Society.Google Scholar
Graff, D., Richling, J. & Werner, M. 2014. jSwarm: distributed coordination in robot swarms. In Proceedings of the International Workshop on Robotic Sensor Networks (RSN).Google Scholar
Groß, R., Bonani, M., Mondada, F. & Dorigo, M. 2006. Autonomous self-assembly in swarm-bots. IEEE Transactions on Robotics 22(6), 1115–1130.Google Scholar
Haken, H. 1984. The Science of Structure: Synergetics. Van Nostrand Reinhold.Google Scholar
Hernandez-Sosa, D., Dominguez-Brito, A. C., Guerra-Artal, C. & Cabrera-Gámez, J. 2005. Runtime self-adaptation in a component-based robotic framework. In IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), 2700–2705. IEEE.Google Scholar
Hertzberg, J., Jaeger, H., Zimmer, U. & Morignot, P 1998. A framework for plan execution in behavior-based robots. In Intelligent Control (ISIC), 1998. Held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), Intelligent Systems and Semiotics (ISAS), Proceedings, 8–13. IEEE.Google Scholar
Hoffmann, J. 2001. FF: the fast-forward planning system. AI Magazine 22(3), 57.Google Scholar
Hrabia, C.-E., Masuch, N. & Albayrak, S. 2015. A metrics framework for quantifying autonomy in complex systems. In Müller, P. J., Ketter, W., Kaminka, G., Wagner, G., and Bulling, N. (eds), Multiagent System Technologies: 13th German Conference, MATES 2015, Cottbus, Germany, September 28–30, 2015, Revised Selected Papers. Springer International Publishing, 22–41.Google Scholar
Joshi, R. K., Carbone, P., Wang, F. C., Kravets, V. G., Su, Y., Grigorieva, I. V., Wu, H. A., Geim, A. K. & Nair, R. R. 2014. Precise and ultrafast molecular sieving through graphene oxide membranes. Science 343(6172), 752–754.Google Scholar
Jung, D. 1998. An architecture for cooperation among autonomous agents. PhD thesis, University of South Australia.Google Scholar
Kaminka, G. A. 2012. Autonomous agents research in robotics: a report from the trenches. In AAAI Spring Symposium: Designing Intelligent Robots.Google Scholar
Kauffman, S. A. 1995. At Home in the Universe: The Search for Laws of Self-Organization and Complexity. Oxford University Press.Google Scholar
Kazadi, S. T. 2000. Swarm engineering. Phd, California Institute of Technology.Google Scholar
Kephart, J. O. & Chess, D. M. 2003. The vision of autonomic computing. Computer 36(1), 41–50.Google Scholar
Kernbach, S., Meister, E., Schlachter, F., Jebens, K., Szymanski, M., Liedke, J., Laneri, D., Winkler, L., Schmickl, T., Thenius, R., Corradi, P. & Ricotti, L. 2008. Symbiotic Robot Organisms: REPLICATOR and SYMBRION Projects. In Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems, PerMIS ’08, 62–69. ACM.Google Scholar
Klavins, E. 2004. A language for modeling and programming cooperative control systems. In 2004 IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA ’04., 4, 3403–3410. IEEE.Google Scholar
Kloetzer, M. & Belta, C. 2006. Hierarchical abstractions for robotic swarms. In Proceedings of the 2006 IEEE International Conference on Robotics and Automation. ICRA 2006, 952–957.Google Scholar
Krupke, D., Ernestus, M., Hemmer, M. & Fekete, S. 2015. Distributed cohesive control for robot swarms: maintaining good connectivity in the presence of exterior forces. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 413–420.Google Scholar
Lee, Y.-S. & Cho, S.-B. 2014. A hybrid system of hierarchical planning of behaviour selection networks for mobile robot control. International Journal of Advanced Robotic Systems 11(4), 1–13.Google Scholar
de Lemos, R., Giese, H., Müller, H. A., Shaw, M., Andersson, J., Litoiu, M., Schmerl, B., Tamura, G., Villegas, N. M., Vogel, T., Weyns, D., Baresi, L., Becker, B., Bencomo, N., Brun, Y., Cukic, B., Desmarais, R., Dustdar, S., Engels, G., Geihs, K., Göschka, K. M., Gorla, A., Grassi, V., Inverardi, P., Karsai, G., Kramer, J., Lopes, A., Magee, J., Malek, S., Mankovskii, S., Mirandola, R., Mylopoulos, J., Nierstrasz, O., Pezzè, M., Prehofer, C., Schäfer, W., Schlichting, R., Smith, D. B., Sousa, J. P., Tahvildari, L., Wong, K. & Wuttke, J. 2013. Software engineering for self-adaptive systems: a second research roadmap. In Software Engineering for Self-Adaptive Systems II: International Seminar, Dagstuhl Castle, Germany, October 24-29, 2010 Revised Selected and Invited Papers, de Lemos, R., Giese, H., Müller, H. & Shaw, M. (eds). Springer Verlag, 1–32.Google Scholar
Maes, P. 1989. How to do the right thing. Connection Science 1(3), 291–323.Google Scholar
Mamei, M., Vasirani, M. & Zambonelli, F. 2005. Self-organizing spatial shapes in mobile particles: The TOTA approach. In Engineering Self-Organising Systems, 138–153. Springer.Google Scholar
Masar, M. 2013. A biologically inspired swarm robot coordination algorithm for exploration and surveillance. In IEEE 17th International Conference on Intelligent Engineering Systems (INES), 271–275.Google Scholar
Matarić, M. J. 1995. Issues and approaches in the design of collective autonomous agents. Robotics and Autonomous Systems 16(2–4), 321–331.Google Scholar
Montagna, S., Viroli, M., Fernandez-Marquez, J. L., Di Marzo Serugendo, G. & Zambonelli, F. 2013. Injecting self-organisation into pervasive service ecosystems. Mobile Networks and Applications 18(3), 398–412.Google Scholar
Morandini, M., Migeon, F., Gleizes, M.-P., Maurel, C., Penserini, L. & Perini, A. 2009. A goal-oriented approach for modelling self-organising MAS. In Engineering Societies in the Agents World X, Lecture Notes in Computer Science. Springer, 33–48.Google Scholar
Naghsh, A. M., Gancet, J., Tanoto, A. & Roast, C. 2008. Analysis and design of human-robot swarm interaction in firefighting. In The 17th IEEE International Symposium on Robot and Human Interactive Communication, 2008. RO-MAN 2008, 255–260. IEEE.Google Scholar
Nagpal, R. 2002. Programmable self-assembly using biologically-inspired multiagent control. In Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 1, 418–425. ACM.Google Scholar
Newman, D. V. 1996. Emergence and strange attractors. Philosophy of Science 63(2), 245–261.Google Scholar
Nicola, R. D., Latella, D., Lafuente, A. L., Loreti, M., Margheri, A., Massink, M., Morichetta, A., Pugliese, R., Tiezzi, F. & Vandin, A. 2015. The SCEL language: design, implementation, verification. In Software Engineering for Collective Autonomic Systems, Wirsing, M., Holzl, M., Koch, N. & Mayer, P. (eds), Lecture Notes in Computer Science 8998. Springer International Publishing, 3–71.Google Scholar
Nicolis, G. 1989. Physics of far-from-equilibrium systems and self-organisation. The New Physics 11, 316–347.Google Scholar
Noël, V. & Zambonelli, F. 2015. Methodological guidelines for engineering self-organization and emergence. Software Engineering for Collective Autonomic Systems, In Wirsing, M., Huolzl, M., Koch, N. & Mayer, P. (eds), Lecture Notes in Computer Science 8998. Springer International Publishing, 355–378.Google Scholar
Nolfi, S. & Floreano, D. 2000. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press Cambridge.Google Scholar
Oreizy, P., Gorlick, M. M., Taylor, R. N., Heimbigner, D., Johnson, G., Medvidovic, N., Quilici, A., Rosenblum, D. S. & Wolf, A. L. 1999. An architecture-based approach to self-adaptive software. IEEE Intelligent Systems 14(3), 54–62.Google Scholar
Penders, J., Alboul, L., Witkowski, U., Naghsh, A., Saez-Pons, J., Herbrechtsmeier, S. & El-Habbal, M. 2011. A Robot Swarm Assisting a Human Fire-Fighter. Advanced Robotics 25(1–2), 93–117.Google Scholar
Picard, G. & Gleizes, M.-P. 2004. The ADELFE methodology. In Methodologies and Software Engineering for Agent Systems, 157–175. Springer.Google Scholar
Pinciroli, C. & Beltrame, G. 2016. Buzz: An extensible programming language for heterogeneous swarm robotics. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3794–3800. IEEE.Google Scholar
Preisler, T., Vilenica, A. & Renz, W. 2013. Decentralized coordination in self-organizing systems based on peer-to-peer coordination spaces. Electronic Communications of the EASST 56, 1–13.Google Scholar
Prokopenko, M. 2009. Guided self-organization. Human Frontiers Science Program (HFSP) Journal 3(5), 287–289.Google Scholar
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R. & Ng, A. Y. 2009a. Ros: an open-source robot operating system. ICRA Workshop on Open Source Software 3 (3.2): 5.Google Scholar
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R. & Ng, A. Y. 2009a. ROS: an open-source Robot Operating System. In ICRA Workshop on Open Source Software 3, 5.Google Scholar
Reina, A., Dorigo, M. & Trianni, V. 2014. Towards a cognitive design pattern for collective decision-making. In Swarm Intelligence, 194–205. Springer International Publishing.Google Scholar
Ren, W. & Beard, A. W. 2004. A decentralized scheme for spacecraft formation flying via the virtual structure approach. AIAA Journal of Guidance, Control, and Dynamics 27, 73–82.Google Scholar
Şahin, E 2005. Swarm robotics: from sources of inspiration to domains of application. In Swarm Robotics, Şahin, E. & Spears, W. M. (eds), Lecture Notes in Computer Science 3342. Springer, 10–20.Google Scholar
Schmeck, H. 2005. Organic computing – a new vision for distributed embedded systems. In 8th IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC), 201–203.Google Scholar
Serugendo, G. D. M., Gleizes, M.-P. & Karageorgos, A. 2005. Self-organization in multi-agent systems. The Knowledge Engineering Review 20(2), 165–189.Google Scholar
Serugendo, G. D. M., Gleizes, M. P. & Karageorgos, A. 2006. Self-organisation and emergence in MAS: an overview. Informatica (Slovenia) 30(1), 45–54.Google Scholar
Steghöfer, J.-P., Seebach, H., Eberhardinger, B. & Reif, W. 2014. PosoMAS: an extensible, modular SE process for open self-organising systems. In PRIMA 2014: Principles and Practice of Multi-Agent Systems, Dam, H. K., Pitt, J., Xu, Y., Governatori, G. & Ito, T. (eds), Lecture Notes in Computer Science 8861. Springer International Publishing, 1–17.Google Scholar
Sudeikat, J., Braubach, L., Pokahr, A., Renz, W. & Lamersdorf, W. 2009. Systematically engineering self-organizing systems: the SodekoVS approach. Electronic Communications of the EASST 17, 1–12.Google Scholar
Sudeikat, J. & Renz, W. 2009. MASDynamics: toward systemic modeling of decentralized agent coordination. In Kommunikation in Verteilten Systemen (KiVS), Informatik aktuell, David, K. & Geihs, K. (eds). Springer, 79–90.Google Scholar
Viroli, M., Casadei, M. & Omicini, A. 2009. A framework for modelling and implementing self-organising coordination. In Proceedings of the 2009 ACM Symposium on Applied Computing, 1353–1360. ACM.Google Scholar
Viscido, S. V., Parrish, J. K. & Grünbaum, D. 2004. Individual behavior and emergent properties of fish schools: a comparison of observation and theory. Marine Ecology Progress Series 273, 239–250.Google Scholar
Wegner, P. 1997. Why interaction is more powerful than algorithms. Communications of the ACM 40(5), 80–91.Google Scholar
Winfield, A. F. T., Harper, C. J. & Nembrini, J. 2004. Towards dependable swarms and a new discipline of swarm engineering. In Swarm Robotics, 126–142. Springer.Google Scholar
Ye, D., Zhang, M. & Vasilakos, A. V. 2017. A survey of self-organization mechanisms in multiagent systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47(3), 441–461.Google Scholar
Yu, C.-H. & Nagpal, R. 2009. Self-adapting modular robotics: A generalized distributed consensus framework. In IEEE International Conference on Robotics and Automation(ICRA), 1881–1888. IEEE.Google Scholar
Yu, C.-H. & Nagpal, R. 2011. A self-adaptive framework for modular robots in a dynamic environment: theory and applications. The International Journal of Robotics Research 30(8), 1015–1036.Google Scholar