Abstract
Hierarchical problem solving is preferred when the problem is overwhelmingly complicated. In such a case, the problem should better be analyzed in hierarchical levels. At each level, some temporary solutions are obtained; then a suitable decision fusion technique is used to merge the temporary solutions for the next level. The hierarchical framework proposed in this study depends on reutilization or elimination of previous level local agents that together perform the decisions due to a decision-fusion technique: a performance criterion is set for local agents. The criterion checks the success of agents in their local regions. An agent satisfying this criterion is reutilized in the next level, whereas an agent not successful enough is removed from the agent pool in the next level. In place of a removed agent, a number of new local agents are developed. This framework is applied on a fault detection problem.
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References
Ben-Arieh D, Easton T (2007) Multi-criteria group consensus under linear cost opinion elasticity. Decis Support Syst 43:713–721
Guo P, Zeng D-Z, Shishido H (2002) Group decision with inconsistent knowledge. IEEE Trans Syst Man Cybern, Part A 32(6):670–679
Herrera F, Martínez L (2001) A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multiexpert decision-making. IEEE Trans Syst Man Cybern, Part B 31:227–234
Huynh VN, Nakamori Y (2005) A satisfactory-oriented approach to multiexpert decision-making under linguistic assessments. IEEE Trans Syst Man Cybern, Part B 35:184–196
Lu J, Shi C, Zhang G (2006) On bilevel multi-follower decision making: general framework and solutions. Inf Sci 176:1607–1627
Mikhailov L (2004) Group prioritization in the AHP by fuzzy preference programming method. Comput Oper Res 31:293–301
Pal BB, Biswas A (2004) A fuzzy multilevel programming method for hierarchical decision making. In: Proceedings of 11th international conference: neural information processing (ICONIP 2004), pp 904–911
Pasi G, Yager RR (2003) Modeling the concept of fuzzy majority opinion. In: Fuzzy sets and systems IFSA 2003, 10th international fuzzy systems association world congress, Istanbul, Turkey. Springer, Berlin, pp 143–150
Pasi G, Yager RR (2006) Modelling the concept of majority opinion in group decision making. Inf Sci 176:390–414
Rahman AFR, Fairhurst MC (1998) A novel confidence-based framework for multiple expert decision fusion. In: Proceedings of 9th British machine vision conference (BMVC 1998), vol 2, pp 205–213
Schuldt A, Werner S (2007) A clustering protocol for team formation based on concept location and time. In: 5th European workshop on multi-agent systems (EUMAS 2007), Hammamet, Tunisia
Simon HA, Dantzig GB, Hogarth R (1986) Decision making and problem solving. National Academy Press, Washington
Tsiporkova E, Boeva V (2006) Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment. Inf Sci 176:2673–2697
Turban E, Aronson JE, Liang T-P (2005) Decision support systems and intelligent systems. Prentice Hall, New York
Umano M, Hatono I, Tamura H (1998) Linguistic labels for expressing fuzzy preference relations in fuzzy group decision making. IEEE Trans Syst Man Cybern, Part B 28(2):205–218
Veeravalli V (1992) Topics in decentralized detection. Doctor of philosophy thesis in electrical engineering, Graduate College of the University of Illinois at Urbana–Champaign, Urbana, Illinois
Viedma H, Martinez L, Mata F (2005) A consensus support system model for group decision-making problems with multi-granular linguistic preference relations. IEEE Trans Fuzzy Syst 13(5):644–658
Wang Y-M, Parkan C (2006) Two new approaches for assessing the weights of fuzzy opinions in group decision analysis. Inf Sci 176:3538–3555
Xu Z (2006) An approach based on the uncertain LOWG and induced uncertain LOWG operators to group decision making with uncertain multiplicative linguistic preference relations. Decis Support Syst 41:488–499
Xu DL, Liu J, Yang JB (2007) Inference and learning methodology of belief-rule-based expert system for pipeline leak detection. Expert Syst Appl 32(1):103–113
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Beldek, U., Leblebicioğlu, K. Local decision making and decision fusion in hierarchical levels. TOP 17, 44–69 (2009). https://doi.org/10.1007/s11750-009-0088-1
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DOI: https://doi.org/10.1007/s11750-009-0088-1