Application of Fuzzy Constraint-Based Agent Negotiation to Collaborative Design - A Case Study for PC Design

• Main Authors: Yen-Kang Han, 韓延康
• Other Authors: K. Robert Lai
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/98160918509742642949

碩士 === 元智大學 === 資訊工程學系 === 92 === A design often requires collaborative working among multidisciplinary engineers. However, the conflicts are mostly arise among sharply diverse viewpoints in the collaborative design process. In such contexts, the key challenge raised by the collaborative design of complex artifacts is how to find an acceptable agreement that maximize the overall preference values of all participants and minimize the disparity among engineers and customers under incomplete and imprecise domain knowledge. Thus, collaborative design as agent negotiation for problem solving, in help of mediating the conflicts, and find ‘win-win’ agreements to meet the whole requirements of all parties. In this thesis, we presents a general problem-solving framework for modeling fuzzy constraint-based agent negotiation, and its extensions to application to collaborative design for PC design in. Meanwhile, we exploit the fuzzy constraints to represent imprecision and uncertainty in PC design process. Finally, the selection of proper two methods: mixed quantitative/qualitative method and ad hoc method. Mixed quantitative/qualitative method is proposed for support designers to resolve conflicts in the design process, which is a constraint-based approach. Ad hoc method by using current design technology of IT industry. We then compare the gain from a low-end product to a high-end product with these methods, and the simulating results show that our method offers higher gain than the mixed quantitative/qualitative method by 3.9%, when designing a low-end product, by 5.85 when designing a middle-end product, and by 6.47% when designing a high-end product. Furthermore, our method similarly offers higher gain than ad hoc method by 2.47%, when designing a middle-end product, by 5.83% when designing a middle-end product, and by 7.72% when designing a high-end product.