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Probability Collectives: A Distributed Optimization Approach

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Probability Collectives

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 86))

Abstract

An emerging Artificial Intelligence tool in the framework of Collective Intelligence (COIN) for modeling and controlling distributed Multi-agent System (MAS) referred to as Probability Collectives (PC) was first proposed by Dr. David Wolpert in 1999 in a technical report presented to NASA.

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References

  1. Wolpert, D.H., Tumer, K.: An introduction to collective intelligence. Technical Report, NASA ARC-IC-99–63, NASA Ames Research Center (1999)

    Google Scholar 

  2. Bieniawski, S.R.: Distributed optimization and flight control using collectives. Ph.D dissertation, Stanford University, CA, USA, (2005)

    Google Scholar 

  3. Wolpert, D.H.: Information theory—the bridge connecting bounded rational game theory and statistical physics. In: Braha, D., Minai, A.A., Bar-Yam, Y. (eds.) Complex Engineered Systems, pp. 262–290. Springer (2006)

    Google Scholar 

  4. Wolpert, D.H., Strauss, C.M.E., Rajnarayan, D.: Advances in distributed optimization using probability collectives. Adv. Complex Syst. 9(4), 383–436 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  5. Wolpert, D.H., Antoine, N.E., Bieniawski, S.R., Kroo, I.R.: Fleet assignment using collective intelligence. In: Proceedings of the 42nd AIAA Aerospace Science Meeting Exhibit (2004)

    Google Scholar 

  6. Bieniawski, S.R., Kroo, I.M., Wolpert, D.H.: Discrete, continuous, and constrained optimization using collectives. In: 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, vol. 5, pp. 3079–3087 (2004)

    Google Scholar 

  7. Huang, C.F., Chang, B.R.: Probability collectives multi-agent systems: a study of robustness in search. LNAI 6422, Part II, pp. 334–343 (2010)

    Google Scholar 

  8. Huang, C.F., Bieniawski, S., Wolpert, D., Strauss, C.E.M.: A comparative study of probability collectives based multiagent systems and genetic algorithms. In: Proceedings of the Conference on Genetic and Evolutionary Computation, pp. 751–752 (2005)

    Google Scholar 

  9. Luo, D.L., Shen, C.L., Wang, B., Wu, W.H.: Air combat decision-making for cooperative multiple target attack using heuristic adaptive genetic algorithm. In: Proceedings of IEEE International Conference on Machine Learning and Cybernetics IEEE Press, pp. 473–478 (2005)

    Google Scholar 

  10. Luo, D.L., Duan, H.B., Wu, S.X., Li, M.Q.: Research on air combat decision-making for cooperative multiple target attack using heuristic ant colony algorithm. Acta Aeronautica et Astronautica Sinica 27(6), 1166–1170 (2006)

    Google Scholar 

  11. Luo, D.L., Yang, Z., Duan, H.B., Wu, Z.G., Shen, C.L.: Heuristic particle swarm optimization algorithm for air combat decision-making on CMTA. Trans. Nanjing Univ. Aeronaut. Astronaut. 23(1), 20–26 (2006)

    MATH  Google Scholar 

  12. Zhang, X.P, Yu, W.H., Liang, J.J., Liu, B.: Entropy regularization for coordinated target assignment. In: Proceedings of 3rd IEEE Conference on Computer Science and Information Technology, pp. 165–169 (2010)

    Google Scholar 

  13. Vasirani, M., Ossowski, S.: Collective-based multiagent coordination: a case study. LNAI 4995, 240–253 (2008)

    Google Scholar 

  14. Modi, P., Shen, W., Tambe, M., Yokoo, M.: Adopt: asynchrous distributed constraint optimization with quality guarantees. Artif. Intell. 161, 149–180 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  15. Mohammad, H.A., Babak, H.K.: A distributed probability collectives optimization method for multicast in CDMA wireless data networks. In: Proceedings of 4th IEEE International Symposium on Wireless Communication Systems, art. No. 4392414, pp. 617–621 (2007)

    Google Scholar 

  16. Ryder, G.S., Ross, K.G.: A probability collectives approach to weighted clustering algorithms for ad hoc networks. In: Proceedings of Third IASTED International Conference on Communications and Computer Networks, pp. 94–99 (2005)

    Google Scholar 

  17. Goldberg, D.E., Samtani, M.P.: Engineering optimization via genetic algorithm. In: Proceedings of 9th Conference on Electronic Computation, pp. 471–484 (1986)

    Google Scholar 

  18. Ghasemi, M.R., Hinton, E., Wood, R.D.: Optimization of trusses using genetic algorithms for discrete and continuous variables. Eng. Comput. 16(3), 272–301 (1999)

    Article  MATH  Google Scholar 

  19. Moh, J., Chiang, D.: Improved simulated annealing search for structural optimization. AIAA J. 38(10), 1965–1973 (2000)

    Article  Google Scholar 

  20. Autry, B.: University course timetabling with probability collectives. Master’s thesis, Naval Postgraduate School Montery, CA, USA (2008)

    Google Scholar 

  21. Sislak, D., Volf, P., Pechoucek, M., Suri, N.: Automated conflict resolution utilizing probability collectives optimizer. IEEE Trans. Syst. Man Cybern.: Appl. Rev. 41(3), 365–375 (2011)

    Article  Google Scholar 

  22. Arora, J.S.: Introduction to Optimum Design. Elsevier Academic Press (2004)

    Google Scholar 

  23. Vanderplaat, G.N.: Numerical Optimization Techniques for Engineering Design. Mcgraw-Hill, New York (1984)

    Google Scholar 

  24. Smyrnakis, M., Leslie, D.S.: Sequentially updated probability collectives. In: Proceedings of 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, pp. 5774–5779 (2009)

    Google Scholar 

  25. Kulkarni, A.J., Tai, K.: Probability collectives for decentralized, distributed optimization: a collective intelligence approach. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 1271–1275 (2008)

    Google Scholar 

  26. Kulkarni, A.J. Tai, K.: Probability collectives: a decentralized, distributed optimization for multi-agent systems. In: Mehnen, J., Koeppen, M., Saad, A., Tiwari, A. (eds.) Applications of Soft Computing, pp. 441–450. Springer (2009)

    Google Scholar 

  27. Kulkarni, A.J., Tai, K.: Solving constrained optimization problems using probability collectives and a penalty function approach. Int. J. Comput. Intell. Appl. 10(4), 445–470 (2011)

    Article  MATH  Google Scholar 

  28. Kulkarni A.J., Tai, K.: A probability collectives approach with a feasibility-based rule for constrained optimization. Appl. Comput. Intell. Soft Comput. 2011, Article ID 980216

    Google Scholar 

  29. Shoham, Y., Powers, R., Grenager, T.: Multi-agent reinforcement learning: a critical survey. www.cc.gatech.edu/~isbell/reading/papers/MALearning.pdf Accessed 23 July 2011

  30. Busoniu, L., Babuska, L., Schutter, B.: A comprehensive survey of multiagent reinforcement learning. IEEE Trans. Syst Man Cybern.—Part C: Appl. Rev. 38(2), 156–172 (2008)

    Google Scholar 

  31. Bowling, M., Veloso, M.: Multiagent learning using a variable learning rate. Artif. Intell. 136(2), 215–250 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  32. Bowling, M., Veloso, M.: Rational and convergent learning in stochastic games. In: Proceedings of 17th International Conference on Artificial Intelligence, pp. 1021–1026 (2001)

    Google Scholar 

  33. Cheng, C.T., Wang, W.C., Xu, D.M., Chau, K.W.: Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos. Water Resour. Manag. 22, 895–909 (2008)

    Article  Google Scholar 

  34. Blumenthal, H.J., Parker, G.B.: Benchmarking punctuated anytime learning for evolving a multi-agent team’s binary controllers. In: Proceedings of World Automation Congress, pp. 1–8 (2006)

    Google Scholar 

  35. Roger, L.S., Tan, M.S., Rangaiah, G.P.: Global optimization of benchmark and phase equilibrium problems using differential evolution. http://www.ies.org.sg/journal/current/v46/v462_3.pdf

  36. Bouvry, P., Arbab, F., Seredynski, F.: Distributed evolutionary optimization, in manifold: rosenbrock’s function case study. Inf. Sci. 122, 141–159 (2000)

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Anand Jayant Kulkarni & Kang Tai

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

Authors
  1. Anand Jayant Kulkarni
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  2. Kang Tai
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  3. Ajith Abraham
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Correspondence to Anand Jayant Kulkarni .

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Kulkarni, A.J., Tai, K., Abraham, A. (2015). Probability Collectives: A Distributed Optimization Approach. In: Probability Collectives. Intelligent Systems Reference Library, vol 86. Springer, Cham. https://doi.org/10.1007/978-3-319-16000-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-16000-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15999-7

  • Online ISBN: 978-3-319-16000-9

  • eBook Packages: EngineeringEngineering (R0)

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