An evidential Markov decision making model

He, Zichang; Jiang, Wen

doi:10.1016/j.ins.2018.08.013

Cited by 85 publications

(46 citation statements)

References 86 publications

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“…The probability distribution function is a useful tool to model the uncertain information, and it has been applied in plenty fields including information fusion, target recognition, and so forth. The probability (belief) distribution function based on the power set also obtained lots of focuses, such as belief conflict, evolutionary games, supplier selection, and failure mode and effects analysis, prediction, and decision making .…”

Section: Introductionmentioning

confidence: 99%

Extension of Yager's negation of a probability distribution based on Tsallis entropy

et al. 2019

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The negation of probability distribution becomes an important topic since some problems are burdensome to deal with directly. Inspired by Yager's negation of probability distribution, an extension model to measure the negation of a probability distribution is proposed using the idea of a nonextensive statistic based on Tsallis entropy. Proofs show that the proposed extension of negation of probability distribution converges to the maximum Tsallis entropy. The proposed model may extend Yager's method to consider the influences of the correlations in a system, which gives the different convergent routes. Some numerical simulation results are used to illustrate the effectiveness of the proposed methodology.

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Section: Introductionmentioning

confidence: 99%

Extension of Yager's negation of a probability distribution based on Tsallis entropy

et al. 2019

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“…Among these methods, Dempster‐Shafer evidence theory is one of the most widely used math tools in many engineering applications such as pattern recognition and decision‐making . Evidence theory has many advantages to handle uncertain information .…”

Section: Introductionmentioning

confidence: 99%

TDBF: Two‐dimensional belief function

Deng

2019

Int J Intell Syst

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How to efficiently handle uncertain information is still an open issue. In this paper, a new method to deal with uncertain information, named as two-dimensional belief function (TDBF), is presented. A TDBF has two components, T = (m m , A B ), both m A and m B are classical belief functions, while m B is a measure of reliable of m A . The definition of TDBF and the discounting algorithm are proposed. Compared with the classical discounting model, the proposed TDBF is more flexible and reasonable. Numerical examples are used to show the efficiency and application of the proposed method. K E Y W O R D S belief function, conflict management, Dempster-Shafer evidence theory, information fusion, two-dimensional belief function, Z-numbers 1 | INTRODUCTIONIt is inevitable to deal with uncertain information in real word. 1-3 There are many articles that take advantage of uncertain information processing problems. 4 To address this issue, many methods have been proposed, such as probability theory, 5 Dempster-Shafer evidence theory, 6,7 fuzzy sets, 8-10 rough sets, 11 Z-numbers, 12 R-numbers, 13,14 and D numbers. [15][16][17][18][19][20] Among these methods, Dempster-Shafer evidence theory 6,7 is one of the most widely used math tools in many engineering applications such as pattern recognition 21,22 and decision-making. 23 Evidence theory has many advantages to handle uncertain information. [24][25][26] For example, the belief function is more flexible to model uncertainty compared with probability distribution. In addition, Dempster's combination rule can combine evidence from different sources without prior information. 27,28 However, it should be pointed out that illogical results may be obtained by classical Dempster combination rule when collected evidence highly conflicts each other. [29][30][31][32][33] Z-numbers is proposed by Zadeh. 12 A Z-number has two components, Z = (A, B). The first component, A, is a restriction (constraint) on the values which a real-valued uncertain variable, X, is allowed to take. The second component, B, is a measure of reliability (certainty) of the first component. 12 Similarly, in evidence theory, discounting coefficient is a measure of sensor report's reliability, for example, discounting factors based on dissimilarity measure, 34 dynamic discounting rates. 35 Nevertheless, the simple value is not enough and reasonable to express the experts evaluation under uncertain situation. To solve this problem, a new math model named as two-dimensional belief function (TDBF) is proposed in this paper.A TDBF is an ordered pair of basic probability assignments (BPAs) denoted as T = (m m , A B ). The first component m A is a classical BPA, which usually is collected from the sensors. The second component m B is a measure of reliability for the first component, which can be collected by the experts. The frame of discernment of m B set Y N Θ = { , }, where "Y" denotes "positive" and "N" denotes "negative." The power set of Y N Θ = { , } consists of two singletons {Y} and {N}, an universal Θ and t...

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“…With the rapid development of human society, the social environment has become more and more complex, which makes us have difficulties in making actual decision [1][2][3][4][5][6][7][8][9]. Therefore, in recent years, more and more attention has been paid to multi-attribute decision making [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Decision Making Method Based on Aggregated Neutrosophic Set

2019

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Multi-attribute decision-making refers to the decision-making problem of selecting the optimal alternative or sorting the scheme when considering multiple attributes, which is widely used in engineering design, economy, management and military, etc. But in real application, the attribute information of many objects is often inaccurate or uncertain, so it is very important for us to find a useful and efficient method to solve the problem. Neutrosophic set is proposed from philosophical point of view to handle inaccurate information efficiently, and a single-valued neutrosophic set (SVNS) is a special case of neutrosophic set, which is widely used in actual application fields. In this paper, a new method based on single-valued neutrosophic sets aggregation to solve multi-attribute decision making problem is proposed. Firstly, the neutrosophic decision matrix is obtained by expert assessment, a score function of single-valued neutrosophic sets (SVNSs) is defined to obtain the positive ideal solution (PIS) and the negative ideal solution (NIS). Then all alternatives are aggregated based on TOPSIS method to make decision. Finally numerical examples are given to verify the feasibility and rationality of the method.

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An evidential Markov decision making model

Cited by 85 publications

References 86 publications

Extension of Yager's negation of a probability distribution based on Tsallis entropy

Extension of Yager's negation of a probability distribution based on Tsallis entropy

TDBF: Two‐dimensional belief function

Multi-Attribute Decision Making Method Based on Aggregated Neutrosophic Set

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