A preference aggregation method through the estimation of utility intervals

Wang, Ying‐Ming; Yang, Jian‐Bo; Xu, Dong-Ling

doi:10.1016/j.cor.2004.01.005

Cited by 212 publications

(98 citation statements)

References 30 publications

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“…It is therefore necessary to compare the interval-valued rewards of all available actions at S and backpropagate the highest one. To do this, the method in [17] to calculate the preference degree between intervals of real numbers is utilised:…”

Section: Team Planningmentioning

confidence: 99%

A Two-Stage Online Approach for Collaborative Multi-agent Planning Under Uncertainty

Palomares

Bauters

Liu

et al. 2016

Lecture Notes in Computer Science

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Abstract. In a team of multiple agents, the pursuance of a common goal is a defining characteristic. Since agents may have different capabilities, and effects of actions may be uncertain, a common goal can generally only be achieved through a careful cooperation between the different agents. In this work, we propose a novel two-stage planner that combines online planning at both team level and individual level through a subgoal delegation scheme. The proposal brings the advantages of online planning approaches to the multi-agent setting. A number of modifications are made to a classical UCT approximate algorithm to (i) adapt it to the application domains considered, (ii) reduce the branching factor in the underlying search process, and (iii) effectively manage uncertain information of action effects by using information fusion mechanisms. The proposed online multi-agent planner reduces the cost of planning and decreases the temporal cost of reaching a goal, while significantly increasing the chance of success of achieving the common goal.

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Section: Team Planningmentioning

confidence: 99%

A Two-Stage Online Approach for Collaborative Multi-agent Planning Under Uncertainty

Palomares

Bauters

Liu

et al. 2016

Lecture Notes in Computer Science

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“…Rodríguez et al [24] proposed the min − upper and max − lower operators to obtain the core information of hesitant fuzzy linguistic assessments of each alternative; then preference degrees [64] are used to deal with multi-criteria linguistic decision making with HFLTSs. Formally, the min − upper and max − lower operators are as follows: Let X = {x 1 , .…”

Section: Definitionmentioning

confidence: 99%

“…On the basis of the core information of each alternative and the preference degrees [64] between two sets of core information, the nondominance degree NDD i of each alternative can be calculated, and the best alternatives are the set of nondominated alternatives X ND = {x i |x i ∈ X, NDD i = max x j ∈X {NDD j }}. The min − upper operator and the max − lower operator are used to obtain the core information of each alternative, such as for alternative…”

Section: Definitionmentioning

confidence: 99%

“…On the basis of H (x 1 ), H (x 2 ) and H (x 3 ), preference degrees [64] between them can be calculated; for example, for H (x 1 ) and H (x 2 ), their preference degree is p 12 = P(a 1 > a 2 ) = max{4−2,0}−max{3−3,0} (4−3)+(3−2) = 1 and the binary preference relation between the three alternatives is Beg and Rashid proposed an alternative hesitant fuzzy linguistic group decision method, that is, the TOPSIS method for HFLTSs [63]. In the TOPSIS method, the main concepts are a distance between two HFLTSs and the HFLTS positive-and negative-ideal solutions, which can be formalized as follows: Let H 1 S and H 2 S be the two HFLTSs on …”

Section: Definitionmentioning

confidence: 99%

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A New Hesitant Fuzzy Linguistic TOPSIS Method for Group Multi-Criteria Linguistic Decision Making

2017

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Hesitant fuzzy linguistic decision making is a focus point in linguistic decision making, in which the main method is based on preference ordering. This paper develops a new hesitant fuzzy linguistic TOPSIS method for group multi-criteria linguistic decision making; the method is inspired by the TOPSIS method and the preference degree between two hesitant fuzzy linguistic term sets (HFLTSs). To this end, we first use the preference degree to define a pseudo-distance between two HFLTSs and analyze its properties. Then we present the positive (optimistic) and negative (pessimistic) information of each criterion provided by each decision maker and aggregate these by using weights of decision makers to obtain the hesitant fuzzy linguistic positive and negative ideal solutions. On the basis of the proposed pseudo-distance, we finally obtain the positive (negative) ideal separation matrix and a new relative closeness degree to rank alternatives. We also design an algorithm based on the provided method to carry out hesitant fuzzy linguistic decision making. An illustrative example shows the elaboration of the proposed method and comparison with the symbolic aggregation-based method, the hesitant fuzzy linguistic TOPSIS method and the hesitant fuzzy linguistic VIKOR method; it seems that the proposed method is a useful and alternative decision-making method.

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“…It is difficult for people to obtain a large amount of historical data of credit guarantee products, so to adopt the models mentioned above to evaluate risks is not an easy task. As for FUZZ and AHP, they are somehow impacted by functions made by people and evaluation indicator weights, therefore, objectivity and reliability can not be assured [7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Decision-Making through Uncertain Linguistic Information by Multi-attribute Group: Concerning Credit Guarantee Products Risk

He¹,

Weng²

2014

Appl. Math. Inf. Sci.

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According to the complexity of credit guarantee products risk, a corresponding risk evaluation system is established here. And uncertain linguistic information is adopted to describe the attribute weight and attribute value of evaluation indicators, and thus a multi-attribute group decision-making model of uncertain linguistic information concerning credit guarantee products risks is established. ULWM and ULHA operators are respectively used in this paper to collect evaluation values of synthesized attribute and overall attribute values of groups. And thus the comprehensive evaluation information of alternatives will be obtained. Then, possibility formula is used to construct a possibility matrix based on which we can decide how to arrange the decision-making schemes. At last, through case analysis, the feasibility and efficiency of evaluation of credit guarantee products risks are demonstrated here.

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A preference aggregation method through the estimation of utility intervals

Cited by 212 publications

References 30 publications

A Two-Stage Online Approach for Collaborative Multi-agent Planning Under Uncertainty

A Two-Stage Online Approach for Collaborative Multi-agent Planning Under Uncertainty

A New Hesitant Fuzzy Linguistic TOPSIS Method for Group Multi-Criteria Linguistic Decision Making

Decision-Making through Uncertain Linguistic Information by Multi-attribute Group: Concerning Credit Guarantee Products Risk

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