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International Journal of Dynamics and Control

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

Behavior-based decision making: a tutorial

  • Mohammad Samadi Gharajeh
Article
  • 42 Downloads

Abstract

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

Keywords

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

List of symbols

F

Feature collection

f

Any feature in the feature collection

B

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

T

Total matrix

\(\upgamma \)

Trust amount in the behavioral matrix

D

Desired set

S

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

R

Success rate

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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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