International Journal of Dynamics and Control

, Volume 6, Issue 4, pp 1816–1840 | Cite as

Behavior-based decision making: a tutorial

  • Mohammad Samadi GharajehEmail author


There are ingenious characteristics in humanistic behaviors so that they can be utilized by the most developers to design smart and complex systems. This paper proposes a novel, knowledge and learning based method called behavior-based decision making, BBDM, in control and system engineering. It is an expert decision support system containing the learning ability to work based on humanistic behavioral reasoning. The knowledge base is built by the system based on various behavioral styles (e.g., safe) associated to other systems and humans. BBDM uses the knowledge-based information to make appropriate decisions when any desired behavioral style is requested from the system. It specifies a success rate for any desired style based on the obtained knowledge base with the aid of a behavioral inference system. This procedure can be used to select a proper system or human to accomplish a requested job. All operations of the BBDM method are performed by a proposed behavioral decision system, called BDS, which consists of three main units: decomposition, behavioral inference, and composition. The decomposition unit splits any behavioral style into several optional features (e.g., safety). The behavioral aggregation sub-unit aggregates all behavioral styles obtained by the system to define the total behavior. The behavioral inference unit produces a success set for any desired behavioral style. Finally, the composition unit converts success set to success rate to specify the success probability of the desired style. Simulation results show that the proposed method has a high efficiency compared to some of the existing decision-making methods.

Graphical Abstract


Decision system Knowledge-based system Learning ability Humanistic behavior Behavioral inference 

List of symbols


Feature collection


Any feature in the feature collection


Behavioral set

\(\upeta \)

The importance factor in the behavioral set

\(\partial \)

The belongingness factor in the behavioral set

\(\upphi \)

The number of decimal places in the identical importance function

\(\hbox {d}_{\mathrm {x}}\)

The degree of any feature in the belongingness functions

\(\hbox {d}_{\mathrm {s}}\)

The degree of the sensible feature in the belongingness functions

\(\hbox {v}_{\mathrm {s}}\)

The value of the sensible feature in the belongingness functions

\(\upalpha \)

The relative factor in the inverse belongingness function

\(\upbeta \)

The relative factor in the relativism belongingness function


Total matrix

\(\upgamma \)

Trust amount in the behavioral matrix


Desired set


Success set

\(\upsigma \)

The difference amount in the inference functions

\(\upvarphi \)

The impact rate of attribute ‘truth’ in the inference functions

\(\upomega \)

The impact rate of attribute ‘growth’ in the inference functions


Success rate


  1. 1.
    Juuso EK (2004) Integration of intelligent systems in development of smart adaptive systems. Int J Approx Reason 35(3):307–337CrossRefGoogle Scholar
  2. 2.
    Hatzilygeroudis I, Prentzas J (2011) Combinations of intelligent methods and applications. Springer, BerlinCrossRefGoogle Scholar
  3. 3.
    Kaparulin DS, Lyakhovich SL, Sharapov AA (2013) BRST analysis of general mechanical systems. J Geom Phys 74:164–184MathSciNetCrossRefGoogle Scholar
  4. 4.
    Lavopa E, Zanchetta P, Sumner M, Cupertino F (2009) Real-time estimation of fundamental frequency and harmonics for active shunt power filters in aircraft electrical systems. IEEE Trans Industr Electron 56(8):2875–2884CrossRefGoogle Scholar
  5. 5.
    Flynn MJ, Luk W (2011) Computer system design: system-on-chip. Wiley, HobokenCrossRefGoogle Scholar
  6. 6.
    Cassidy AS, Georgiou J, Andreou AG (2013) Design of silicon brains in the nano-CMOS era: spiking neurons, learning synapses and neural architecture optimization. Neural Netw 45:4–26CrossRefGoogle Scholar
  7. 7.
    Bueno-Crespo A, García-Laencina PJ, Sancho-Gómez J-L (2013) Neural architecture design based on extreme learning machine. Neural Netw 48:19–24CrossRefGoogle Scholar
  8. 8.
    Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501CrossRefGoogle Scholar
  9. 9.
    Alpaydin E (2014) Introduction to machine learning. MIT Press, CambridgezbMATHGoogle Scholar
  10. 10.
    Ho W, Xu X, Dey PK (2010) Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. Eur J Oper Res 202(1):16–24CrossRefGoogle Scholar
  11. 11.
    Mela K, Tiainen T, Heinisuo M (2012) Comparative study of multiple criteria decision making methods for building design. Adv Eng Inform 26(4):716–726CrossRefGoogle Scholar
  12. 12.
    Harris J (2001) An introduction to fuzzy logic applications, vol 23. Springer, BerlinGoogle Scholar
  13. 13.
    Azadegan A, Porobic L, Ghazinoory S, Samouei P, Kheirkhah AS (2011) Fuzzy logic in manufacturing: a review of literature and a specialized application. Int J Prod Econ 132(2):258–270CrossRefGoogle Scholar
  14. 14.
    Fragiadakis NG, Tsoukalas VD, Papazoglou VJ (2014) An adaptive neuro-fuzzy inference system (anfis) model for assessing occupational risk in the shipbuilding industry. Saf Sci 63:226–235CrossRefGoogle Scholar
  15. 15.
    Hadjileontiadou SJ, Dias SB, Diniz JA, Hadjileontiadis LJ (2015) Fuzzy logic-based modeling in collaborative and blended learning. IGI global in advances in educational technologies and instructional design (AETID) seriesGoogle Scholar
  16. 16.
    Eiben AE, Schoenauer M (2002) Evolutionary computing. Inf Process Lett 82(1):1–6MathSciNetCrossRefGoogle Scholar
  17. 17.
    Yang S, Li M, Liu X, Zheng J (2013) A grid-based evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 17(5):721–736CrossRefGoogle Scholar
  18. 18.
    Hung D (2001) Theories of learning and computer-mediated instructional technologies. Educ Media Int 38(4):281–287CrossRefGoogle Scholar
  19. 19.
    Durand G, Belacel N, LaPlante F (2013) Graph theory based model for learning path recommendation. Inf Sci 251:10–21CrossRefGoogle Scholar
  20. 20.
    Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRefGoogle Scholar
  21. 21.
    Yurish SY (2010) Smart sensors systems design: new curricula based on marie curie chairs excellence (EXC) project’s results. Measurement 43(1):21–30CrossRefGoogle Scholar
  22. 22.
    Lei C-U, Wan K, Man KL (2013) Developing a smart learning environment in universities via cyber-physical systems. Procedia Comput Sci 17:583–585CrossRefGoogle Scholar
  23. 23.
    Gintis H, Bowles S, Boyd R, Fehr E (2003) Explaining altruistic behavior in humans. Evolut Hum Behav 24(3):153–172CrossRefGoogle Scholar
  24. 24.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefGoogle Scholar
  25. 25.
    Almond RG, Mislevy RJ, Steinberg LS, Yan D, Williamson DM (2015) Bayesian networks in educational assessment. Springer, BerlinCrossRefGoogle Scholar
  26. 26.
    Cai B, Huang L, Xie M (2017) Bayesian networks in fault diagnosis. IEEE Trans Industr Inf 13(5):2227–2240CrossRefGoogle Scholar
  27. 27.
    Soualhi A, Razik H, Clerc G, Doan DD (2014) Prognosis of bearing failures using hidden Markov models and the adaptive neuro-fuzzy inference system. IEEE Trans Industr Electron 61(6):2864–2874CrossRefGoogle Scholar
  28. 28.
    Suparta W, Alhasa KM (2016) Adaptive neuro-fuzzy interference system. In: Modeling of tropospheric delays using ANFIS. Springer, pp 5–18Google Scholar
  29. 29.
    Robb A, White C, Cordar A, Wendling A, Lampotang S, Lok B (2015) A comparison of speaking up behavior during conflict with real and virtual humans. Comput Hum Behav 52:12–21CrossRefGoogle Scholar
  30. 30.
    Ristea I (2013) Reflections on mechanisms influencing human behavior. Procedia-Soc Behav Sci 92:799–805CrossRefGoogle Scholar
  31. 31.
    Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616CrossRefGoogle Scholar
  32. 32.
    Marston S, Li Z, Bandyopadhyay S, Zhang J, Ghalsasi A (2011) Cloud computing–the business perspective. Decis Support Syst 51(1):176–189CrossRefGoogle Scholar
  33. 33.
    Gharajeh MS (2015) The significant concepts of cloud computing: technology, architecture, applications, and security. CreateSpace Independent Publishing PlatformGoogle Scholar
  34. 34.
    Gupta RD, Kundu D (2001) Generalized exponential distribution: different method of estimations. J Stat Comput Simul 69(4):315–337MathSciNetCrossRefGoogle Scholar
  35. 35.
    Pedrycz W (1994) Why triangular membership functions? Fuzzy Sets Syst 64(1):21–30MathSciNetCrossRefGoogle Scholar
  36. 36.
    Zhao J, Bose BK (2002) Evaluation of membership functions for fuzzy logic controlled induction motor drive. In: IEEE 28th annual conference of the industrial electronics society (IECON), 5–8, vol 1, pp 229–234Google Scholar
  37. 37.
    Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13CrossRefGoogle Scholar
  38. 38.
    Ross TJ (2004) Fuzzy logic with engineering applications, 2nd edn. Wiley, HobokenzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Young Researchers and Elite Club, Tabriz BranchIslamic Azad UniversityTabrizIran

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