Many interaction processes in complex adaptive systems occur in groups, and in order to organize knowledge, collaboration and a proper distribution of functions and tasks, there is a need to analyze, model and develop computational systems in which several autonomous units interact, adapt and work together in a common open environment, combining individual strategies into overall behavior. The approach to engineering a desired system-level behavior, adopted in this work, is based on a multi-agent system [11], in which the preferred responses emerge as a result of inter-agent interactions.
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References
Albert R, Jeong H, Barabási A-L (1999) Diameter of the world-wide web. Nature, 401: 130-131.
Albert R, Jeong H, Barabási A-L (2000) Error and attack tolerance of complex networks. Nature, 406: 378-382.
Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1): 47-97.
Butler M, Prokopenko M, Howard T (2001) Flexible synchronisation within RoboCup environment: a comparative analysis. In: Stone P, Balch TR, Kraet-zschmar GK (eds) RoboCup 2000: Robot Soccer World Cup IV (Proc. 4th RoboCup-2000 Workshop, 31 August - 1 September, Melbourne, Australia), Lecture Notes in Computer Science 2019: 119-128, Springer-Verlag, Berlin.
Bonabeau E, Theraulaz G, Deneubourg J-L, Camazine S (1997) Self-organisation in social insects. Trends in Ecology and Evolution, 12 (5): 188-193.
Boschetti F, Prokopenko M, Macreadie I, Grisogono A-M (2005) Defining and detecting emergence in complex networks. In: Khosla R, Howlett RJ, Jain LC (eds) Proc. 9th Intl. Conf. Knowledge-Based Intelligent Information and Engi-neering Systems - KES 2005, 14-16 September, Melbourne, Australia, Lecture Notes in Computer Science 3684: 573-580, Springer-Verlag, Berlin.
Dhamala M, Lai YC, Kostelich EJ (2001) Analyses of transient chaotic time series. Physical Review E, 64: 1-9.
Dorigo M, Gambardella LM (1997) Ant colonies for the Traveling Salesman Problem. BioSystems, 43: 73-81.
Durrant-Whyte, HF, Stevens M (2001) Data fusion in decentralised sensing networks. Proc. 4th Intl. Conf. Information Fusion, 7-10 August, Montreal, Canada, International Society of Information Fusion, Sunnyvale, CA.
Faloutsos M, Faloutsos P, Faloutsos C (1999) On power-law relationships of the internet topology. Computer Communication Review, 29: 251-262.
Ferber J (1999) Multi-Agent Systems. Addison Wesley Professional, Reading, MA.
Foreman M, Prokopenko M, Wang P (2003) Phase transitions in self-organising sensor networks. In: Banzhaf W, Christaller T, Dittrich P, Kim JT, Ziegler J (eds) Advances in Artificial Life (Proc. 7th European Conf. Artificial Life -ECAL2003), 14-17 September, Dortmund, Germany, Lecture Notes in Artificial Intelligence 2801: 781-791, Springer-Verlag, Berlin.
Gerasimov V, Healy G, Prokopenko M, Wang P, Zeman A (2006) Symbiotic sensor networks in complex underwater terrains: a simulation framework. In: Gabrys B, Howlett RJ, Jain LC (eds) Proc. 10th Intl. Knowledge-Based Intel-ligent Information and Engineering Systems Conf. - KES 2006, 9-11 October, Bournemouth, UK, Lecture Notes in Artificial Intelligence 4253(III): 315-323, Springer-Verlag, Berlin.
Ghanem M, Guo Y, Hassard J, Osmond M, Richards M (2004) Sensor grid for air pollution monitoring. In: Cox SJ (ed) Proc. 3rd UK e-Science All-hands Conf. - AHM 2004, 31 August - 3 September, Nottingham, UK, Engineering and Physical Sciences Research Council (EPSRC), UK: 106-113.
Grassberger P, Procaccia I (1983) Estimation of the Kolmogorov entropy from a chaotic signal. Physical Review A, 28(4): 2591-2593.
Jánosi IM, Tél T (1994) Time series analysis of transient chaos. Physical Review E, 49(4): 2756-2763.
Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási, A-L (2000) The large-scale organization of metabolic networks. Nature, 407: 651-654.
Jones T, James G (2005) The management and control of distributed energy resources(extended version). In: Proc. 18th Intl. Conf. and Exhibition on Electricity Distribution - CIRED, June 2005, Turin, Italy, IEE, London, UK: 987-998.
Kolmogorov AN (1959) Entropy per unit time as a metric invariant of automorphisms (in Russian). Doklady Akademii Nauk SSSR, 124: 754-755.
Lin R, Gerla M (1997) Adaptive clustering for mobile wireless networks. IEEE J. Selected Areas in Communications, September: 1265-1275.
Makarenko A, Kaupp T, Grocholsky B, Durrant-Whyte HF (2003) Human-robot interactions in active server networks. In: Computational Intelligence in Robotics and Automation for the New Millennium (Proc. 2003 IEEE Intl. Symposium Computational Intelligence in Robotics and Automation), 16-20 July, Kobe, Japan, IEEE Computer Society Press, Piscataway, NJ, 1: 247-252.
Mathews GM, Durrant-Whyte HF, Prokopenko M (2006) Scalable decen-tralised decision making and optimisation in heterogeneous teams. In: Proc. IEEE Intl. Conf. Multisensor Fusion and Integration for Intelligent Systems -MFI2006, 3-6 September, Heidelberg, Germany, IEEE Computer Society Press, Piscataway, NJ: 383-388.
Newman MEJ (2002) The structure and function of networks. In: Hossfeld F, Binder E (eds) Proc. Europhysics Conf. Computational Physics - CCP2001, 5-8 September, 2001, Aachen, Germany, Elsevier Science, Amsterdam, 147(1-2): 40-45.
Ogston E, Overeinder B, Van Steen M, Brazier F (2003) A Method for decentral-ized clustering in large multi-agent systems. In: Rosenschein JS, Sandholm T, Wooldridge M, Yokoo M (eds) Proc. 2nd Intl. Joint Conf. Autonomous Agents and Multi-Agent Systems, 14-18 July, Melbourne, Australia, ACM Press, New York, NY: 798-796.
Olsson L, Nehaniv CL, Polani D (2004) Sensory channel grouping and struc-ture from uninterpreted sensor data. In: Zebulum RS, Gwaltney D, Hornby G, Keymeulen D, Lohn J, Stoica A (eds) Proc. NASA/DoD Conf. Evolvable Hard-ware - EH’04, 24-26 June, Seattle, WA, IEEE Computer Society Press, Los Alamitos, CA: 153-160.
Piraveenan M, Prokopenko M, Wang P, Price DC (2005) Towards adaptive clustering in self-monitoring multi-agent networks. In: Khosla R, Howlett RJ, Jain LC (eds) Proc. 9th Intl. Conf. Knowledge-Based Intelligent Information and Engineering Systems - KES’2005, 14-16 September, Melbourne, Australia. Lecture Notes in Computer Science 3682(II), Springer-Verlag, Berlin: 796-805.
Prokopenko M (1999) On situated reasoning in multi-agent systems. In: Hybrid Systems and AI: Modeling, Analysis and Control of Discrete and Continuous Systems, AAAI Technical Report SS-99-05, March, AAAI Press, Menlo Park, CA: 158-163.
Prokopenko M, Butler M, Howard T (2001) On emergence of scalable tactical and strategic behavior. In: Stone P, Balch TR, Kraetzschmar GK (eds) RoboCup 2000: Robot Soccer World Cup IV (Proc. 4th RoboCup-2000 Workshop), 31 August - 1 September, Melbourne, Australia, Lecture Notes in Computer Science 2019, Springer-Verlag, Berlin: 357-366.
Prokopenko M, Wang P (2004) On self-referential shape replication in robust aerospace vehicles. In: Pollack J, Bedau MA, Husbands P, Ikegami T, Watson RA (eds) Artificial Life IX (Proc. 9th Intl. Conf. Simulation and Synthesis of Living Systems), 12-15 September, Boston, MA, MIT Press, Cambridge, MA: 27-32.
Prokopenko M, Wang P (2004) Evaluating team performance at the edge of chaos. In: Polani D, Browning B, Bonarini A, Yoshida K (eds) RoboCup 2003: Robot Soccer World Cup VII (Proc. 7th RoboCup-2003 Springer-Verlag, Berlin:Symposium), Padua, July, Lecture Notes in Computer Science 3020: 89-101.
Prokopenko M, Piraveenan M, Wang P (2005) On convergence of dynamic clus-ter formation in multi-agent networks. In: Capcarrére MS, Freitas AA, Bentley PJ, Johnson CG, Timmis J (eds) Advances in Artificial Life (Proc. 8th European Conference - ECAL 2005), 5-9 September, Canterbury, UK. Lecture Notes in Computer Science 3630, Springer-Verlag, Berlin: 884-894.
Prokopenko M, Wang P, Price DC, Valencia P, Foreman M, Farmer AJ (2005) Self-organising hierarchies in sensor and communication networks. Artificial Life (Special issue on Dynamic Hierarchies), 11(4): 407-426.
Prokopenko M, Wang P, Foreman M, Valencia P, Price DC, Poulton G (2005) On connectivity of reconfigurable impact networks in ageless aerospace vehicles. J. Robotics and Autonomous Systems, 53: 36-58.
Prokopenko M, Wang P, Price DC (2005) Complexity metrics for self-monitoring impact sensing networks. In: Lohn J, Gwaltney D, Hornby G, Zebulum R, Keymeulen D, Stoica A (eds) Proc. NASA/DoD Conf. Evolvable Hardware -EH-05, 29 June - 1 July, Washington, DC, IEEE Computer Society Press, Los Alamitos, CA: 239-246.
Prokopenko M, Poulton GT, Price DC, Wang P, Valencia P, Hoschke N, Farmer AJ, Hedley M, Lewis C, Scott DA (2006) Self-organising impact sensing net-works in robust aerospace vehicles. In: Fulcher, J (ed) Advances in Applied Artificial Intelligence. Idea Group, Hershey, PA: 186-223.
Prokopenko M, Gerasimov V, Tanev I (2006) Evolving spatiotemporal coor-dination in a modular robotic system. In: Nolfi S, Baldassarre G, Calabretta R, Hallam JCT, Marocco D, Meyer J-A, Miglino O, Parisi D (eds) From Ani-mals to Animats 9 (Proc. 9th Intl. Conf. Simulation of Adaptive Behavior -SAB2006), 25-29 September, Rome, Italy, Lecture Notes in Computer Science 4095, Springer-Verlag, Berlin: 558-569.
Pynadath DV, Tambe M (2002) Multiagent teamwork: analyzing the optimality and complexity of key theories and models. In: Castelfranchi C, Johnson WL (eds) Proc. 1st Intl. Joint Conf. Autonomous Agents and Multiagent Systems -AAMAS2002, 15-19 July, Bologna, Italy, ACM Press, New York, NY: 873-880.
Rasmussen S, Baas NA, Mayer B, Nilsson M, Olesen MW (2001) Ansatz for dynamical hierarchies. Artificial Life, 7(4): 329-353.
Rényi, A(1970) Probability theory. North-Holland, Amsterdam, The Netherlands.
Sandholm T, Lesser V (1995) Coalition formation among bounded rational agents. In: Mellish C (ed) Proc. 14th Intl. Joint Conf. Artificial Intelligence -IJCAI95, 20-25 August, Montréal, Québec, Canada, Morgan Kaufmann, San Francisco, CA: 662-671.
Sinai YG (1959) On the concept of entropy of a dynamical system (in Russian). Doklady Akademii Nauk SSSR, 124: 768-771.
Solé RV, Ferrer-Cancho R, Montoya JM, Valverde S (2002) Selection, tinkering and emergence in complex networks - crossing the land of tinkering. Complexity, 8(1): 20-33.
Takens F (1981) Detecting strange attractors in turbulence. Dynamical systems and Turbulence, Lecture Notes in Mathematics 898, Springer-Verlag, Berlin: 366-381.
US Department of Energy (2006) Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them. A Report to the United Stated Congress Pursuant to Section 1252 of the Energy Policy Act of 2005, February.
Wieselthier JE, Nguyen GD, Ephremides A (2000) On the construction of energy-efficient broadcast and multicast trees in wireless networks. Proc. 19th Annual Joint Conf. IEEE Computer and Communications Societies -INFOCOM2000, 26-30 March, Tel Aviv, Israel: 585-594.
White JG, Southgate E, Thompson JN, Brenner S (1986) The structure of the nervous system of the nematode C. elegans. Philosophical Transactions of the Royal Society of London - Series B: Biological Sciences, 314(1165): 1-340.
Williams, RJ, Martinez ND (2000) Simple rules yield complex food webs. Nature, 404: 180-183.
Ygge F, Akkermans JM (1996) Power load management as a computational mar-ket. In: Tokoro M (ed) Proc. 2nd Intl. Conf. Multi-Agent Systems - ICMAS’96, 9-13 December, Kyoto, Japan, AAAI Press, Menlo Park, CA: 393-400.
Zhang T, Ramakrishnan R, Livny M (1997) BIRCH: a new data clustering algorithm and its applications. Data Mining and Knowledge Discovery, 1(2): 141-182.
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Piraveenan, M., Prokopenko, M., Wang, P., Zeman, A. (2008). Decentralized Multi-Agent Clustering in Scale-free Sensor Networks. In: Fulcher, J., Jain, L.C. (eds) Computational Intelligence: A Compendium. Studies in Computational Intelligence, vol 115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78293-3_12
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