A Fuzzy Dynamic Belief Logic System

Jing, Xiaoxin; Luo, Xudong; Zhang, Youzhi

doi:10.1002/int.21652

Cited by 13 publications

(14 citation statements)

References 46 publications

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“…The values of two parameters must be subjectively chosen in the proposed methodology, namely the values of ξ and ζ, which represent the fixed allowances that are added to or subtracted from the cycle time estimate. Experts may choose various values for these parameters; therefore, various estimates must be aggregated . In addition, even when the experts use FBPNs with the same settings, the estimation results differ among runs because the initial values of the parameters are randomized.…”

Section: Methodsmentioning

confidence: 99%

An Efficient and Effective Fuzzy Collaborative Intelligence Approach for Cycle Time Estimation in Wafer Fabrication

Chen

2015

Int. J. Intell. Syst.

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A common characteristic of fuzzy collaborative forecasting methods is the establishment of an interval estimate that is guaranteed to include the actual value. Such a characteristic is crucial to various planning purposes because it reduces the risk of incorrect forecasting. A new fuzzy collaborative forecasting method is proposed in this study to estimate the cycle time of a job in a wafer fabrication factory; this is a critical task for managing the fabrication of wafers. The proposed fuzzy collaborative forecasting method uses a fuzzy back propagation network that effectively improves the precision of estimation. The accuracy is enhanced through expert collaboration. The performance of the proposed methodology was selectively compared with those of two existing fuzzy collaborative forecasting methods by applying them to a real case. The results confirmed that the proposed methodology substantially enhanced the performance in estimating the cycle time of a job.

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Section: Methodsmentioning

confidence: 99%

An Efficient and Effective Fuzzy Collaborative Intelligence Approach for Cycle Time Estimation in Wafer Fabrication

Chen

2015

Int. J. Intell. Syst.

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“…Some forms of fuzzy set have also been developed such as interval valued fuzzy sets (Zadeh 1973), vague sets (Gau and Buehrer 1993), rough fuzzy sets (Yang and Hinde 2010), and intuitionistic fuzzy sets and interval-valued intuitionistic fuzzy sets (Atanassov 1986(Atanassov , 1994. New theoretical and application fields of fuzzy set theory, such as the theory of fuzzy operators (Yager and Kacprzyk 1997;Xu 2005;Tan and Chen 2011;Meng and Tang 2013;Jing et al 2014;Zhou 2014) have also emerged. Manu applications (Bellman and Zadeh 1970;Zadeh 1996;Liao 2001;Adali et al 2009;Chen and Wang 2009;Jeguirim et al 2011;Chen 2012Chen , 2013 have shown the interest of fuzzy set based modeling.…”

Section: Cloud-based Design For Configurations Modelmentioning

confidence: 97%

Fuzzy holons for intelligent multi-scale design in cloud-based design for configurations

et al. 2015

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Cloud-based design for configurations can be referred to as a service-oriented networked design for configurations model. However, cloud-based models also pose challenges such as reliability, availability, capability, ability, adaptability of resources, and services across spatial boundaries. Multi-scale design can presumably stimulate greater intelligence in cloud-based models. Using the concepts of the fuzzy holon and the fuzzy attractor, this paper proposes the fuzzy holonic approach to address multi-scale design for configurations. A fuzzy design holon is defined through two basic holons: fuzzy function holon and fuzzy solution holon. A fuzzy attractor is defined as a fuzzy function holon or fuzzy function solution toward which a design tends to evolve. The proposed fuzzy holon model is driven by two conflicting drives: (a) completeness of fuzzy function holons and fuzzy solution holons, and (b) discrimination of fuzzy function holons and fuzzy solution holons. Through simulations, four possible states of behavior of fuzzy holon design are found: (a) the impossibility state characterized by the impossibility of fuzzy holon creation; (b) the creation and destruction state sometimes characterized by the creation of fuzzy holons and sometimes the destruction of fuzzy holons, (c) the development state characterized by a natural creation and development of fuzzy holons and (d) the failure state characterized by the interruption of the development of the fuzzy design holon and the destruction of already created fuzzy design holon. The model explains that design is not an orderly and well behaved phenomenon. It shows that fuzzy holon design is a discontinuous phenomenon.Keywords Intelligent design · Cloud-based design and manufacturing · Cloud-based design for configuration · Multi scale design · Fuzzy holon design

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“…In the above definition, formula is actually a specific uninorm, which is introduced by Yager and Rybalov and generalized further by Luo et al . Uninom operators have been applied into many domains such as automated negotiation, evidence aggregation in law, and fuzzy reasoning …”

Section: A Fuzzily Constrained Model For Gamesmentioning

confidence: 99%

Games Played under Fuzzy Constraints

Zhang

Luo

Leung

2015

Int. J. Intell. Syst.

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Psychological experiment studies reveal that human interaction behaviors are often not the same as what game theory predicts. One of important reasons is that they did not put relevant constraints into consideration when the players choose their best strategies. However, in real life, games are often played in certain contexts where players are constrained by their capabilities, law, culture, custom, and so on. For example, if someone wants to drive a car, he/she has to have a driving license. Therefore, when a human player of a game chooses a strategy, he/she should consider not only the material payoff or monetary reward from taking his/her best strategy and others' best responses but also how feasible to take the strategy in that context where the game is played. To solve such a game, this paper establishes a model of fuzzily constrained games and introduces a solution concept of constrained equilibrium for the games of this kind. Our model is consistent with psychological experiment results of ultimatum games. We also discuss what will happen if Prisoner's Dilemma and Stag Hunt are played under fuzzy constraints. In general, after putting constraints into account, our model can reflect well the human behaviors of fairness, altruism, self‐interest, and so on, and thus can predict the outcomes of some games more accurate than conventional game theory.

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A Fuzzy Dynamic Belief Logic System

Cited by 13 publications

References 46 publications

An Efficient and Effective Fuzzy Collaborative Intelligence Approach for Cycle Time Estimation in Wafer Fabrication

An Efficient and Effective Fuzzy Collaborative Intelligence Approach for Cycle Time Estimation in Wafer Fabrication

Fuzzy holons for intelligent multi-scale design in cloud-based design for configurations

Games Played under Fuzzy Constraints

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