專家設計師搜尋策略之設計思考研究

Abstract

設計問題處理的行為是人類在思考行為中是深層而且高度複雜的表現，此種行為經由記憶系統的運作與決策處理的過程，在心智與認知上展現了人類對於訊息處理的複雜能力；另外，設計這種有目的性的運用智慧改變或創造新事物的行為，如同人類的問題解決行為，可經由學習的累積，在設計問題的處理上發展出更快速有效的問題解決模式，而這些設計策略的應用也正反應設計師對設計問題的不同解決能力。以往這些被視為難以解釋的設計行為，現正透過認知科學領域的各項相關研究逐步的打開這個神秘的人類思考「黑箱」，同時也使得設計師對設計問題處理的各種策略被逐步釐清，這樣的發展，使得透過過搜尋理論模型，來發掘設計活動過程中的現象成為可能。 在設計過程中，專家設計師對於設計問題處理的能力，展現出高度的效率及完整性，但以往的研究卻對於專家設計師在設計問題處理過程中的搜尋策略有相異的觀點，也就是有著前推式搜策傾向及倒推式搜策傾向的問題。 本研究主要針對此點，對專家設計師在設計活動過程中的行為，從設計問題解決點上架構的完善性與處理效率的影響進行探討。實驗中利用工業設計案例，先以口語分析法進行認知實驗，使用二組編碼系統及二次編碼進行，接著再將此一部分在認知實驗中所獲得的結果帶入人工智慧的領域進行討論，也就是透過電腦模擬實驗中的狀況，以期將人類專家的搜尋機制帶入電腦運作機制，來探討電腦處理複雜問題能力，同時也導入設計中常見的回溯處理機制，透過認知科學與人工智慧領域的交互驗證，發現專家設計師對設計問題架構具有良好的以做為往後在設計過程進行中的良好基礎，而在設計問題的處理上專家設計師則傾向由前推式策略進行設計問題空間的搜尋，而在問題處理的考量順序上則與設計的關鍵性因素及設計目標息息相關。

The behavior of how to deal with problems of design is supposed to be high-context and absolutely complicated expression. Since this method is through memory-system exercising and decision-making, it displays human's complicated ability of information handling acknowledgeably and mentally. On the other hand, the case of designing these objective ways by exercising intelligence to create is just the same as human's problem-solving--via large amounts of learning to develop fast and efficient problem-solving models. And often the application of these design strategies can reflect designer's ability to solve different design problem. In the past, those viewed as unexplainable behaviors of design are gradually revealed by lots of related researches and also the mysterious "Black box" is studied through cognition-science sphere. At the same time, designers have much clearer mind toward different strategies of problem-solving. Such a development makes "via searching theory model to excavate the phenomenon during the activity" possible. During processing of design, expert designers perform great efficiency and completion. However, in the past, some studies show that expert designers' viewpoints may differ in searching strategy; that is expert designers tend to use working-forward or working-backward searching strategies. Therefore, this study focuses on the process while expert designers are designing activities. They start to probe into the completion of designing problem-solving and efficiency while real practicing. On experiment, first adopt the case of industrial design which starts from protocol analysis to proceed cognition studies . Then bring the result from the cognition experiment into artificial intelligence sphere to discuss, which means through computer simulated experiment, we expect to bring the search strategies of expert designers into computerizing to consider the abilities of computers managing complicated problems. Meanwhile, input those common reverse mechanism. Besides, because cognitive science and artificial intelligence verifying for each other, we find out that a good problem-designing frame can lay a firm foundation in future designing. And as for design problem-solving, expert designers tend to choose working-forward strategy to search the possibility of design problem-solving. At last, the sequence of problem-solving are highly related with the critical causes and the target of the design.