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  本文選題：多屬性協(xié)商 + 貝葉斯學習��； 參考：《山東大學》2014年碩士論文

【摘要】：近年來,云計算模式的勢頭愈演愈烈,其理念在制造業(yè)逐步興起,很多計算機服務中心,把資源虛擬化為服務,并集中起來建立云服務平臺。云制造的概念也應運而生。大量服務的聚集在更好地滿足了服務需求者的各種需要的同時,也給服務分配帶來了巨大挑戰(zhàn)。作為服務分配中的一個重要問題,服務交易成為一個重要的研究課題。 鑒于服務購買者的多樣化需求以及自己出售服務的迫切需要,服務提供者需要以各種方式展示并銷售自己的服務,因此,建立多種交易模式是服務提供者也是云平臺交易系統(tǒng)的基本需要。目前主要的交易模式有固定價格、協(xié)商、拍賣等,相較于協(xié)商而言,固定價格和拍賣交易的研究成果已經比較成熟。 而制造業(yè)和服務業(yè)目前正呈現(xiàn)出一種日益精細化和專業(yè)化的趨勢,“按需定制”和“按需使用”的理念也越來越深入人心,服務購買者的需求表現(xiàn)在多個屬性上。專業(yè)化服務制造鏈的建立,也使得服務提供者之間的服務交易在多個議題上存在異議。因此,在開放和動態(tài)的云服務平臺上,多屬性協(xié)商機制是解決服務交易問題的最好選擇,也因此而成為很多領域的重要研究對象。 不同于單屬性協(xié)商(即傳統(tǒng)的價格協(xié)商)中協(xié)商雙方最終只能實現(xiàn)“單贏”,多屬性協(xié)商是可以獲得“雙贏”的。雙贏不僅有利于單個協(xié)商實例中交易合約的達成,更能促使協(xié)商參與方之間的長期合作,實現(xiàn)服務提供者的長久利益。雙贏的實現(xiàn)主要是因為協(xié)商參與方在多個屬性的不同偏好,因此在協(xié)商中獲取對方偏好信息十分重要。 因此,本文首先提出了一種基于貝葉斯學習機制的雙贏多屬性協(xié)商策略,通過學習對方的偏好信息,來指導協(xié)商策略達成雙贏。同時,為了提高協(xié)商策略的有效性,本文也對學習算法做出了兩點改進：第一,本文提出了雙向假設空間的概念,用于改善對方代理協(xié)商行為出錯時的協(xié)商結果；第二,本文還提出了一種基于協(xié)商屬性讓步先后序列的假設概率更新方法,使代理不必關心對方的保留效用值和讓步率,從而在很大程度上提高學習結果的精度。 針對多種協(xié)商模式的實現(xiàn)問題,本文提出了一種基于多模式的交易系統(tǒng)過程管理方案。目前雖然很多人在交易模式領域展開了研究,但交易系統(tǒng)的真正實現(xiàn)和應用仍然發(fā)展緩慢,且多數原型集中在單模式交易系統(tǒng),這與交易過程管理方面的考慮不足有很大關系。因此我們提供了一個基于云平臺服務交易系統(tǒng)的多模式交易過程管理架構,并給出了交易過程的流程和管理方法。另外,我們對每個交易模式采用web service和BPEL來實現(xiàn),來實現(xiàn)單模式流程的平臺獨立性、靈活性、可實現(xiàn)性等特點。
[Abstract]:In recent years, the cloud computing model is becoming more and more powerful, and its concept is gradually rising in the manufacturing industry. Many computer service centers virtualize resources as services, and focus on building cloud service platform. The concept of cloud manufacturing came into being. The agglomeration of a large number of services not only better meets the needs of service demanders, but also brings great challenges to service distribution. As an important issue in service distribution, service transaction has become an important research topic. Given the diverse needs of service buyers and the urgent need to sell their own services, service providers need to showcase and sell their services in a variety of ways, The establishment of a variety of transaction models is the basic needs of service providers and cloud platform trading systems. At present, the main trading models are fixed price, negotiation, auction and so on. Compared with negotiation, the research results of fixed price and auction transaction have been relatively mature. At present, manufacturing and service industries are showing a trend of refinement and specialization. The concepts of "on demand customization" and "on demand" are becoming more and more popular, and the demand of service buyers is manifested in a number of attributes. The establishment of specialized service manufacturing chain also makes the service transaction between service providers different on many issues. Therefore, on the open and dynamic cloud service platform, the multi-attribute negotiation mechanism is the best choice to solve the problem of service transaction, and therefore becomes an important research object in many fields. Different from single attribute negotiation (that is, traditional price negotiation), negotiation can only achieve "single win", and multi-attribute negotiation can achieve "win-win". The win-win situation is not only conducive to the conclusion of the transaction contract in a single negotiation instance, but also to the long-term cooperation between the negotiation participants and the realization of the long-term benefit of the service provider. The realization of win-win situation is mainly due to the different preferences of negotiation participants in multiple attributes, so it is very important to obtain the preference information of each other in negotiation. Therefore, this paper first proposes a win-win multi-attribute negotiation strategy based on Bayesian learning mechanism, which can guide the negotiation strategy to achieve win-win by learning the preference information of the other party. At the same time, in order to improve the effectiveness of negotiation strategy, this paper also makes two improvements to the learning algorithm: first, this paper proposes the concept of bidirectional hypothesis space, which is used to improve the negotiation results when the negotiation behavior of the other agent is wrong. This paper also proposes a method of updating the hypothesis probability based on the negotiation attribute concession sequence, which makes the agent do not have to care about the retention utility value and concession rate of the other side, so as to improve the accuracy of the learning results to a great extent. In this paper, a process management scheme based on multi-mode transaction system is proposed to solve the problem of multi-negotiation mode. Although many people have carried out research in the field of transaction model, the real realization and application of trading system is still slow, and most prototypes are concentrated in single-mode trading system, which is related to the insufficient consideration of transaction process management. Therefore, we provide a multi-mode transaction process management architecture based on cloud platform service transaction system, and give the process and management method of transaction process. In addition, we use web service and BPEL to realize the platform independence, flexibility and realizability of single mode flow.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.09

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本文編號：1892239