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2019 | OriginalPaper | Buchkapitel

Towards Fully Probabilistic Cooperative Decision Making

verfasst von : Miroslav Kárný, Zohreh Alizadeh

Erschienen in: Multi-Agent Systems

Verlag: Springer International Publishing

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Abstract

Modern prescriptive decision theories try to support the dynamic decision making (DM) in incompletely-known, stochastic, and complex environments. Distributed solutions single out as the only universal and scalable way to cope with DM complexity and with limited DM resources. They require a solid cooperation scheme, which harmonises disparate aims and abilities of involved agents (human decision makers, DM realising devices and their mixed groups). The paper outlines a distributed fully probabilistic DM. Its flat structuring enables a fully-scalable cooperative DM of adaptive and wise selfish agents. The paper elaborates the cooperation based on sharing and processing agents’ aims in the way, which negligibly increases agents’ deliberation effort, while preserving advantages of distributed DM. Simulation results indicate the strength of the approach and confirm the possibility of using an agent-specific feedback for controlling its cooperation.

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The axiomatisation [19] also shows that any Bayesian DM formulation can be approximated by an FPD formulation to an arbitrary precision.

Aknine, S., Caillou, P., Pinson, S.: Searching Pareto optimal solutions for the problem of forming and restructuring coalitions in multi-agent systems. Group Decis. Negot. 19, 7–37 (2010)CrossRef

Azizi, S., Quinn, A.: Hierarchical fully probabilistic design for deliberator-based merging in multiple participant systems. IEEE Trans. Syst. Man Cybern. PP(99), 1–9 (2016). https://doi.org/10.1109/TSMC.2016.2608662CrossRef

Barndorff-Nielsen, O.: Information and Exponential Families in Statistical Theory. Wiley, Hoboken (1978)MATH

Bond, A., Gasser, L.: Readings in Distributed Artificial Intelligence. Morgan Kaufmann, Burlington (2014)

Chen, L., Pu, P.: Survey of preference elicitation methods. Technical report IC/2004/67, HCI Group Ecole Politechnique Federale de Lausanne, Switzerland (2004)

Daee, P., Peltola, T., Soare, M., Kaski, S.: Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction. Mach. Learn. 106(9), 1599–1620 (2017)MathSciNetCrossRef

DeGroot, M.: Optimal Statistical Decisions. McGraw-Hill, New York City (1970)MATH

Genest, C., Zidek, J.: Combining probability distributions: a critique and annotated bibliography. Stat. Sci. 1(1), 114–148 (1986)MathSciNetCrossRef

Guan, P., Raginsky, M., Willett, R.: Online Markov decision processes with Kullback Leibler control cost. IEEE Trans. Autom. Control 59(6), 1423–1438 (2014)MathSciNetCrossRef

10.

Harsanyi, J.: Games with incomplete information played by Bayesian players. I-III. Management Science 50(Suppl. 12), 1763–1893 (2004)

11.

Kárný, M.: Adaptive systems: local approximators? In: Workshop on Adaptive Systems in Control and Signal Processing, pp. 129–134. IFAC, Glasgow (1998)

12.

Kárný, M.: Recursive estimation of high-order Markov chains: approximation by finite mixtures. Inf. Sci. 326, 188–201 (2016)CrossRef

13.

Kárný, M.: Implementable prescriptive decision making. In: Guy, T., Kárný, M., D., D.R.I., Wolpert, D. (eds.) Proceedings of the NIPS 2016 Workshop on Imperfect Decision Makers, vol. 58, pp. 19–30. JMLR (2017)

14.

Kárný, M., et al.: Optimized Bayesian Dynamic Advising: Theory and Algorithms. Springer, London (2006)

15.

Kárný, M., Guy, T.V.: Fully probabilistic control design. Syst. Control Lett. 55(4), 259–265 (2006)MathSciNetCrossRef

16.

Kárný, M., Guy, T.V., Bodini, A., Ruggeri, F.: Cooperation via sharing of probabilistic information. Int. J. Comput. Intell. Stud. 1, 139–162 (2009)CrossRef

17.

Kárný, M., Guy, T.: On support of imperfect Bayesian participants. In: Guy, T., et al. (eds.) Decision Making with Imperfect Decision Makers. Intelligent Systems Reference Library, vol. 28, pp. 29–56. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-24647-0_2CrossRef

18.

Kárný, M., Herzallah, R.: Scalable harmonization of complex networks with local adaptive controllers. IEEE Trans. SMC: Syst. 47(3), 394–404 (2017)

19.

Kárný, M., Kroupa, T.: Axiomatisation of fully probabilistic design. Inf. Sci. 186(1), 105–113 (2012)MathSciNetCrossRef

20.

Kulhavý, R., Zarrop, M.B.: On a general concept of forgetting. Int. J. Control 58(4), 905–924 (1993)MathSciNetCrossRef

21.

Kullback, S., Leibler, R.: On information and sufficiency. Ann. Math. Stat. 22, 79–87 (1951)MathSciNetCrossRef

22.

Lewicki, R., Weiss, S., Lewin, D.: Models of conflict, negotiation and 3rd party intervention - a review and synthesis. J. Organ. Behav. 13(3), 209–252 (1992)CrossRef

23.

Mattingley, J., Wang, Y., Boyd, S.: Receding horizon control. IEEE Control Syst. Mag. 31(3), 52–65 (2011)MathSciNetCrossRef

24.

Mayne, D.: Model predictive control: recent developments and future promise. Automatica 50, 2967–2986 (2014)MathSciNetCrossRef

25.

Mine, H., Osaki, S.: Markovian Decision Processes. Elsevier, New York (1970)MATH

26.

Nelsen, R.: An Introduction to Copulas. Springer, Heidelberg (1999). https://doi.org/10.1007/978-1-4757-3076-0CrossRefMATH

27.

Nurmi, H.: Resolving group choice paradoxes using probabilistic and fuzzy concepts. Group Decis. Negot. 10, 177–198 (2001)CrossRef

28.

Peterka, V.: Bayesian system identification. In: Eykhoff, P. (ed.) Trends and Progress in System Identification, pp. 239–304. Pergamon Press, Oxford (1981)CrossRef

29.

Savage, L.: Foundations of Statistics. Wiley, Hoboken (1954)MATH

30.

Sečkárová, V.: Cross-entropy based combination of discrete probability distributions for distributed decision making. Ph.D. thesis, Charles University in Prague, Faculty of Mathematics and Physics, Department of Probability and Mathematical Statistics., Prague (2015). Submitted in May 2015. Successfully defended on 14 September 2015

31.

Sečkárová, V.: Weighted probabilistic opinion pooling based on cross-entropy. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9490, pp. 623–629. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26535-3_71CrossRefMATH

32.

Sečkárová, V.: On supra-Bayesian weighted combination of available data determined by Kerridge inaccuracy and entropy. Pliska Stud. Math. Bulgar. 22, 159–168 (2013)MathSciNetMATH

33.

Shore, J., Johnson, R.: Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. IEEE Trans. Inf. Theory 26(1), 26–37 (1980)MathSciNetCrossRef

34.

Simpson, E.: Combined decision making with multiple agents. Ph.D. thesis, Hertford College, Department of Engineering Science, University of Oxford (2014)

35.

Simpson, E., Roberts, S., Psorakis, I., Smith, A.: Dynamic Bayesian combination of multiple imperfect classifiers. In: Guy, T., Kárný, M., Wolpert, D. (eds.) Decision Making and Imperfection. Studies in Computation Intelligence, vol. 474, pp. 1–35. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-36406-8_1CrossRef

36.

Todorov, E.: Linearly-solvable Markov decision problems. In: Schölkopf, B., et al. (eds.) Advances in Neural Information Processing, pp. 1369–1376. MIT Press, NY (2006)

37.

Šindelář, J., Vajda, I., Kárný, M.: Stochastic control optimal in the Kullback sense. Kybernetika 44(1), 53–60 (2008)MathSciNetMATH

38.

Wald, A.: Statistical Decision Functions. Wiley, London (1950)MATH

39.

Wallenius, J., Dyer, J., Fishburn, P., Steuer, R., Zionts, S., Deb, K.: Multiple criteria decision making, multiattribute utility theory: recent accomplishments and what lies ahead. Manag. Sci. 54(7), 1336–1349 (2008)CrossRef

40.

Zlotkin, G., Rosenschein, J.: Mechanism design for automated negotiation and its applicatin to task oriented domains. Artif. Intell. 86, 195–244 (1996)CrossRef

Titel: Towards Fully Probabilistic Cooperative Decision Making
verfasst von: Miroslav Kárný
Zohreh Alizadeh
Verlag: Springer International Publishing
Buch: Multi-Agent Systems
Print ISBN: 978-3-030-14173-8

Electronic ISBN: 978-3-030-14174-5

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-14174-5_11

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