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A-Teams and Their Applications

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Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems (ICCCI 2009)

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Introduction

The techniques used to solve difficult combinatorial optimization problems have evolved from constructive algorithms to local search techniques, and finally to population-based algorithms. Population-based methods have become very popular. They provide good solutions since any constructive method can be used to generate the initial population, and any local search technique can be used to improve each solution in the population. But population-based methods have the additional advantage of being able to combine good solutions in order to get possibly better ones. The basic idea behind this way of doing is that good solutions often share parts with optimal solutions.

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References

  1. Aydin, M.E., Fogarty, T.C.: Teams of asynchronous agents for job-shop scheduling  problems: an experimental study. Journal of Intelligent Manufacturing 15(4), 455–462 (2004)

    Article  Google Scholar 

  2. Baerentzen, L., Avila, P., Talukdar, S.: Learning Network Designs for Asynchronous  Teams. In: MAAMAW, pp. 177–196 (1997)

    Google Scholar 

  3. Barbucha, D., Czarnowski, I., Jedrzejowicz, P., Ratajczak, E., Wierzbowska, I.: JADE-Based A-Team as a Tool for Implementing Population-Based Algorithms. In: Chen, Y., Abraham, A. (eds.) Intelligent Systems Design and Applications, IDSA, Jinan Shandong China, pp. 144–149. IEEE, Los Alamitos (2006)

    Google Scholar 

  4. Barbucha, D., Jedrzejowicz, P.: Multi-agent platform for solving the dynamic vehicle routing problem. In: Proc. 11th Int. IEEE Conference on Intelligent Transportation Systems, Beijing, pp. 517–522 (2008)

    Google Scholar 

  5. Barbucha, D., Jedrzejowicz, P.: An experimental investigation of the synergetic effect of multiple agents working together in the A-Team. System Science 34(2), 55–62 (2008)

    MATH  Google Scholar 

  6. Barbucha, D., Czarnowski, I., Jedrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: An Implementation of the JADE-base A-Team Environment. International Transactions on Systems Science and Applications 3(4), 319–328 (2008)

    Google Scholar 

  7. Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: Web accessible A-team middleware. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part III. LNCS, vol. 5103, pp. 624–633. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  8. Barbucha, D.: Simulating Activities of the Transportation Company through Multi-Agent System Solving the Dynamic Vehicle Routing Problem. In: Hakansson, A., et al. (eds.) KES AMSTA 2009. LNCS (LNAI), vol. 5559, pp. 773–782. Springer, Heidelberg (2009)

    Google Scholar 

  9. Bellifemine, F., Caire, G., Poggi, A., Rimassa, G.: JADE. A White Paper, Exp. 3(3), 6–20 (2003)

    MATH  Google Scholar 

  10. Beni, G.: The concept of cellular robotic system. In: Proceedings of the 1988 IEEE International Symposium on Intelligent Control, pp. 57–62. IEEE Computer Society Press, Los Alamitos (1988)

    Google Scholar 

  11. Beni, G., Wang, J.: Swarm intelligence. In: Proceedings of the Seventh Annual Meeting of the Robotics Society of Japan, pp. 425–428. RSJ Press, Tokyo (1989)

    Google Scholar 

  12. Beni, G.S.: Hackwood: Stationary waves in cyclic swarms. In: Proceedings of the 1992 International Symposium on Intelligent Control, pp. 234–242. IEEE Computer Society Press, Los Alamitos (1992)

    Chapter  Google Scholar 

  13. Blum, J., Eskandarian, A.: Enhancing Intelligent Agent Collaboration for Flow Optimization of Railroad Traffic. Transportation Research 36(10), 919–930 (2002)

    Article  Google Scholar 

  14. Camponogara, E., Talukdar, S.: Designing communication networks for distributed control agents. European Journal of Operational Research 153, 544–563 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  15. Chen, C.L., Talukda, S.N.: Causal Nets for Fault Diagnosis. In: 4th International Conference on Expert Systems Application to Power Systems, Melbourne, Australia, pp. 65–70 (1993)

    Google Scholar 

  16. Cheyer, A., Martin, D.: The open agent architecture. Journal of Autonomous Agents and Multi-Agent Systems 4(1), 1438 (2001)

    Google Scholar 

  17. Correa, R., Gomes, F.C., Oliveira, C., Pardalos, P.M.: A parallel implementation of an asynchronous team to the point-to-point connection problem. Parallel Computing 29, 447–466 (2003)

    Article  MathSciNet  Google Scholar 

  18. Craig, I.: Blackboard systems. Alex Publishing Co., Norwood (1995)

    Google Scholar 

  19. Czarnowski, I., Jedrzejowicz, P.: An agent-based approach to ANN training. Knowledge-Based Systems 19, 304–308 (2006)

    Article  Google Scholar 

  20. Czarnowski, I., Jȩdrzejowicz, P.: An agent-based approach to the multiple-objective selection of reference vectors. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 117–130. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  21. Czarnowski, I., Jedrzejowicz, P.: An Agent-Based Algorithm for Data Reduction. In: Bramer, M., et al. (eds.) Research and Development in Intelligent Systems XXIV, pp. 351–356. Springer, Heidelberg (2007)

    Google Scholar 

  22. Czarnowski, I., Jędrzejowicz, P., Wierzbowska, I.: An A-team approach to learning classifiers from distributed data sources. In: Nguyen, N.T., Jo, G.-S., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2008. LNCS (LNAI), vol. 4953, pp. 536–546. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  23. Czarnowski, I., Jȩdrzejowicz, P.: Data reduction algorithm for machine learning and data mining. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds.) IEA/AIE 2008. LNCS (LNAI), vol. 5027, pp. 276–285. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  24. Czarnowski, I., Jȩdrzejowicz, P.: A framework for adaptive and integrated classification. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 522–532. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  25. Czarnowski, I.: Distributed Data Reduction through Agent Collaboration. In: Hakansson, A., et al. (eds.) KES AMSTA 2009. LNCS (LNAI), vol. 5559, pp. 724–733. Springer, Heidelberg (2009)

    Google Scholar 

  26. Czarnowski, I., Jedrzejowicz, P., Wierzbowska, I.: A-Team Middleware on a Cluster. In: Hakansson, A., et al. (eds.) KES AMSTA 2009. LNCS (LNAI), vol. 5559, pp. 764–772. Springer, Heidelberg (2009)

    Google Scholar 

  27. Dreżewski, R.: A model of co-evolution in multi-agent system. In: Mařík, V., Müller, J.P., Pěchouček, M. (eds.) CEEMAS 2003. LNCS (LNAI), vol. 2691, pp. 314–323. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  28. Far, B.H.: Modeling and implementation of software agents decision making. In: Proceedings of the Third IEEE International Conference on Cognitive Informatics, pp. 258–267 (2004)

    Google Scholar 

  29. Glover, F.: Tabu search and adaptive memory programming advances, applications and challenges. In: Barr, R.S., et al. (eds.) Advances in Metaheuristics, Optimization and Stochastic Modelling Technologies, pp. 1–75. Kluwer Academic Publishers, Boston (1997)

    Google Scholar 

  30. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  31. Gorti, S., Humair, R.D., Sriram, S.N., Talukdar, S.: Solving constraint satisfaction problems using A-Teams. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, 1–19 (1996)

    Article  Google Scholar 

  32. Jedrzejowicz, P.: Population learning algorithm and its applications. In: Olariu, S., Zomaya, A.Y. (eds.) Handbook of Bioinspired Algorithms and Applications, pp. 555–588. Chapman & Hall, Boca Raton (2006)

    Google Scholar 

  33. Jędrzejowicz, P., Wierzbowska, I.: JADE-based A-team environment. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 719–726. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  34. Jedrzejowicz, J., Jedrzejowicz, P.: An investigation of agent-based hybrid approach to solve flowshop and job-shop scheduling problems. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part I. LNCS (LNAI), vol. 4692, pp. 172–179. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  35. Jędrzejowicz, P., Ratajczak-Ropel, E.: Agent-based approach to solving the resource constrained project scheduling problem. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4431, pp. 480–487. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  36. Jederzejowicz, P., Ratajczak-Ropel, E.: Solving the RCPSP/max Problem by the Team of Agents. In: Hakansson, A., et al. (eds.) KES AMSTA 2009. LNCS (LNAI), vol. 5559, pp. 734–743. Springer, Heidelberg (2009)

    Google Scholar 

  37. Jennings, N., Sycara, K., Wooldridge, M.: A roadmap of agent research and development. Autonomous Agent and Multi-Agent Systems 1, 7–38 (1998)

    Article  Google Scholar 

  38. Kao, J.H., Hemmerle, J.S., Prinz, F.B.: Collision avoidance using asynchronous teams. In: 1996 IEEE International Conference on Robotics and Automation, vol. 2, pp. 1093–1100 (1996)

    Google Scholar 

  39. Kisiel-Dorohinicki, M.: Agent-oriented model of simulated evolution. In: Grosky, W.I., Plášil, F. (eds.) SOFSEM 2002. LNCS, vol. 2540, pp. 253–261. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  40. Koubarakis, M.: Multi-agent systems and peer-to-peer computing: Methods, systems, and challenges. In: Klusch, M., Omicini, A., Ossowski, S., Laamanen, H. (eds.) CIA 2003. LNCS (LNAI), vol. 2782, pp. 46–61. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  41. Lesser, R.: Cooperative Multiagent Systems: A Personal View of the State of the Art. IEEE Transactions on Knowledge and Data Engineering 11(1), 133–142 (1999)

    Article  Google Scholar 

  42. Meneses, C.N., Pardalos, P.M., Ragle, M.: Asynchronous Teams for probe selection problems. Discrete Optimization 5, 74–87 (2008)

    Article  MATH  Google Scholar 

  43. Murthy, S., Rachlin, J., Akkiraju, R., Wu, F.: Agent-Based Cooperative Scheduling. In: Charniak, E.C. (ed.) Constraints and Agents, AAAI-1997, pp. 112–117 (1997)

    Google Scholar 

  44. Müller, J.P.: The right agent (Architecture) to do the right thing. In: Rao, A.S., Singh, M.P., Müller, J.P. (eds.) ATAL 1998. LNCS (LNAI), vol. 1555, pp. 211–225. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  45. Liau, C.J.: Belief, information acquisition, and trust in multi-agent systems - A modal logic formulation. Artificial Intelligence 149(1), 31–60 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  46. Olariu, S., Zomaya, A.Y. (eds.): Handbook of Bioinspired Algorithms and Applications. Chapman & Hall, Boca Raton (2006)

    MATH  Google Scholar 

  47. Paredis, J.: Coevolutionary computation. Artificial Life 2(4), 355–375 (1995)

    Article  Google Scholar 

  48. Potter, M.A., De Jong, K.A.: Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29 (2000)

    Article  Google Scholar 

  49. Rabak, C.S., Sichman, J.S.: Using A-Teams to optimize automatic insertion of electronic components. Advanced Engineering Informatics 17, 95–106 (2003)

    Article  Google Scholar 

  50. Rachlin, J., Goodwin, R., Murthy, S., Akkiraju, R., Wu, F., Kumaran, S., Das, R.: A-teams: An agent architecture for optimization and decision-support. In: Rao, A.S., Singh, M.P., Müller, J.P. (eds.) ATAL 1998. LNCS (LNAI), vol. 1555, pp. 261–276. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  51. Taillard, E.D., Gambardella, L.M., Gendreau, M., Potvin, J.-Y.: Adaptive memory programming: A unified view of metaheuristics. European Journal of Operational Research 135, 1–16 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  52. Talukdar, S.N., Pyo, S.S., Giras, T.: Asynchronous Procedures for Parallel Processing. IEEE Trans. on PAS 102(11), 3652–3659 (1983)

    Article  Google Scholar 

  53. Talukdar, S.N., de Souza, P.S.W.: Scale Efficient Organizations. In: Proc. 1992 IEEE International Conference on Systems, Man, and Cybernetics, Chicago, Il, pp. 1458–1463 (1992)

    Google Scholar 

  54. Talukdar, S.N., de Souza, P., Murthy, S.: Organizations for Computer-Based Agents. Engineering Intelligent Systems 1(2), 56–69 (1993)

    Google Scholar 

  55. Talukdar, S.N., Ramesh, V.C.: A muti-agent technique for contingency constrained optimal power flows. IEEE Transactions on Power Systems 9(2), 855–861 (1994)

    Article  Google Scholar 

  56. Talukdar, S., Baerentzen, L., Gove, A., de Souza, P.: Asynchronous teams: cooperation schemes for autonomous agents. Journal of Heuristics 4(4), 295–332 (1998)

    Article  MATH  Google Scholar 

  57. Talukdar, S.N.: Collaboration rules for autonomous software agents. Decision Support Systems 24, 269–278 (1999)

    Article  Google Scholar 

  58. Tweedale, J., Ichalkaranje, N., Sioutis, C., Jarvis, B., Consoli, A., Phillips-Wren, G.E.: Innovations in multi-agent systems. Journal of Network and Computer Applications 30(3), 1089–1115 (2007)

    Article  Google Scholar 

  59. Vahidov, R., Fazlollahi, B.: Pluralistic multi-agent decision support system: a framework and an empirical test. Information & Management 41, 883–898 (2004)

    Article  Google Scholar 

  60. Yang, H., Zhang, C.: Definition and application of a comprehensive framework for distributed problem solving. In: Distributed Artificial Intelligence, Architecture and Modeling, First Australian Workshop on DAI, Canberra, pp. 1–16 (1995)

    Google Scholar 

  61. Zhang, C.: Cooperation under uncertainty in distributed expert systems. Artificial Intelligence 56, 21–69 (1992)

    Article  MathSciNet  Google Scholar 

  62. Zhu, Q.: Topologies of agents interactions in knowledge intensive multi-agent systems for networked information services. Advanced Engineering Informatics 20, 31–45 (2006)

    Article  Google Scholar 

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Authors and Affiliations

  1. Chair of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Piotr Jȩdrzejowicz

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  1. Piotr Jȩdrzejowicz
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Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Str. Janiszweskiego 11/17, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Centre for Complex Software Systems and Services, Swinburne University of Technology, Hawthorn, P.O. Box 218, 3122, Victoria, Australia

    Ryszard Kowalczyk

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, G#43, Sec. 4, Keelung Rd, 106, Taipei, Taiwan, R.O.C.

    Shyi-Ming Chen

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Jȩdrzejowicz, P. (2009). A-Teams and Their Applications. In: Nguyen, N.T., Kowalczyk, R., Chen, SM. (eds) Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems. ICCCI 2009. Lecture Notes in Computer Science(), vol 5796. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04441-0_3

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  • DOI: https://doi.org/10.1007/978-3-642-04441-0_3

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