This paper presents an intelligent, hybrid system for stamping process planning in progressive die design. The system combines the flexibility of blackboard architecture with case-based reasoning. The hybrid system has the advantage that it can use past knowledge and experience for case-based reasoning when it exists, and other reasoning approaches when it doesn’t exist. A prototype system has been implemented in CLIPS and interfaced with Solid Edge CAD system. An example is included to demonstrate the approach. / Singapore-MIT Alliance (SMA)

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傳統的資產負債管理(Asset-Liability Management，ALM)研究大多強調數量分析方法，並未考慮資料來源的問題。然而在銀行實務上，資產負債管理人員卻必須根據現有內外部資料來釐定資產負債組合的整体政策。在決策支援系統中，模式整合的功能包含模式之組合及連結等，可用以整合數量分析模式與相關資料。本研究運用人工智慧技術來探討資產負債管理中模式整合之問題。藉此可以明瞭ALM的分析流程，以作為銀行人員訓練之參考。另一方面由於應用黑板架構發展系統，也可以提供一個有彈性的整合環境，以反應使用者需求及資料異動狀況，亦可彈性新增、刪除及修改模式整合過程中的資料結構與知識內涵，以為未來連接理論技巧與實務環境之參考。 / The computer support for Asset Liability Management (ALM) in the literature emphasizes on the mathematical analysis and does not address the data source problems. In the practical banking environment, however, ALM decisions are made based on the dynamic internal and external data changes. Therefore, an ideal ALM decision support system has to consider the integration of data sources and mathematical analysis. Traditional Decision Support Systems (DSS) rely on the expert's assistance to understand the problem and formulate or integrate appropriate models. There is a growing recognition that incorporates Artificial Intelligence techniques (Al) into the DSS can enhance the acceptance of these decision aids by management. 　　This paper intends to develop an Intelligent Decision Support System (TDSS) and addresses the model integration concept for the ALM. In the paper, model integration is defined as a series of processes from which important decision making information is inferred through automatic data model mapping and mathematical model conversion. The investigation of model integration concept helps the ALM analysis process understanding which can be useful for baaldng personnel training. On the other hand, the IDSS provides a flexible integration environment in which the system can flexibly response to the user's analysis request with the updated data situations. Since the blackboard architecture used for the system development supports the modularization structure, its inherent maintainability aLows a flexible update of the domain knowledge and data structure, and can therefore serve as a testbed to evaluate the potential integration approaches of various ALM data and mathematical models.

智慧型決策支援系統(Intellignet Decision Support Systems)的特點是可以同時處理定性和定量資料於同一個系統中，以同時執行各種知識推論及數量模式之運算。黑板架構(Blackboard Architecture)的做法是將決策支援求解過程的資料、模式和知識運用情形記錄於一共同工作區─稱之為黑板(Blackboard)，將模式及知識記錄在所謂的知識源(Knowledge Sources)中，並提供較有彈性之控制機制，應可提供較佳的解釋功能。以黑板架構為基礎的智慧型系統多應用在科學及工程方面，在管理方面卻寥寥無幾；管理問題多半屬於半結構性或非結構性，良好的解釋應為智慧型決策系統之重要功能。本研究擬就銀行業之授信審查做為本系統之專業領域知識(Domain Knowledge)，運用黑板架構中的階層化問題表現方式及模組化知識源分類之特性，建立提供完善解釋功能之智慧型授信決策支援系統。 / Incorporating artificial intelligence (AI) technique is critical to improve the functionality of decision support systems. Explanation function for consultation-based systems has been emphasized in the literature and should be considered important in developing intelligent decision support systems. Blackboard architecture can support a well-organized explanation facility due to its structurization of problem solving space, modularization of domain knowledge, and flexibility of reasoning control. Applying blackboard systems to managerial domain gets attention recently. Since most managerial consultation problems are unstructured or semi-structured, good explanation facility should be able to enhance the consultation effectiveness. The thesis investigates the potential of developing an explanation facility on the blackboard architecture using the loan evaluation as an example. During the interactive consultation process, the system can answer questions such as "What?", "Why?", "How?", and "Where?" with a friendly user interface. In terms of contribution, the inclusion of explanation facility can potentially increase the willingness and confidence of decision makers in using intelligent decision support systems. On the other hand, applying the graphic user interface to the development of explanation facility based on the blackboard architecture can make the reasoning process transparent and enhance the acceptance of this AI technique to managerial problem solving.