2017年英國《經濟學人》雜誌曾提出，「世界上最寶貴的資源不再是石油，而是數據」。隨著物聯網時代來臨，工業應用領域也開始整合各種技術而掀起新一波工業革命。因為大量自動化及數據化，除了升級自動化設備、整合網通系統，監控設備產生的大數據，透過工業電腦進行分析，經由人工智能判斷邏輯產生條件，再由設備自主處理各種生產問題。除去大量勞動，專注於大數據自動化處理，即能生產更優質的產品，並且優化流程，降低企業成本。 自動化流程機器人(Robotic Process Automation)能自動的管理並執行企業大量耗費時間與人力的業務流程，可用於客戶服務、人力管理、供應鏈管理、採購、會計等範疇。物聯網(IoT)時代下的機器人自動化流程加入了認知運算等新興技術，更能進一步提升企業效率並降低成本。自動化流程機器人(Robotic Process Automation)儼然成下一個新的生產力革命。 市場研究機構IDC預測，2017年全球在認知和人工智慧系統支出將達到125億美元，和2016年相比成長達59.3%。Google母公司Alphabet公開測試無人駕駛汽車、阿里宣佈投資千億成立達摩院、百度機器人入駐肯德基等等。人工智慧(Artificial Intelligence)將顛覆商業思維、改寫商業模式。在2020年，人工智慧(Artificial Intelligence)將成為市場上真正的「主流」技術思維。IDC並且認為亞洲將在2020年成為全球第二大認知與人工智慧輸出區域。 本文探討自動化流程機器人與人工智慧之間的關聯，以及流程優化後對企業所產生的影響與變革．並且針對個案的自動化解決方案所達到的效益與後續發展進行評估與檢討，藉以提升自動化解決方案，協助企業在未來挑戰的競爭環境中創造最佳化優勢． / “The Economist” stated in 2017 that “the world’s most precious resource is no longer oil but data”. With the advent of the Internet of Things, industrial applications have begun to integrate various technologies and set off a new wave of industrial revolution. Because of a large amount of automation and data, in addition to upgrading automation soluitons, integrating netcom systems, and monitoring the big data generated by the solutions, analysis is performed through industrial computers, and conditions are generated through the logic judgment of artificial intelligence, and then the solutions autonomously handles various processes. It can produce better products, optimize the process and reduce business costs to focus on automation of big data and to save a lot of labor hiring. Robotic Process Automation can automate the management and execution of a large number of business processes that consume time and manpower, and can be used in areas such as customer service, manpower management, supply chain management, procurement, finance and accounting. The robotic automation process in the Internet of Things (IoT) era has added emerging technologies such as cognitive computing to further enhance the efficiency of enterprises and to reduce costs. Robotic Process Automation becomes the next new productivity revolution. In 2017, marketing research firm, IDC, predicts that global spendings on cognitive and artificial intelligence systems will reach US$12.5 billion, which represents a growth of 59.3% compared to 2016. Google, the parent company of Alphabet, publicly tests driverless cars, Ali announced that it has invested 100 billion to establish Daruma House, Baidu Robots has settled in Kentucky. Artificial Intelligence will disrupt business thinking and rewrite business models. In 2020, Artificial Intelligence will become the real "mainstream" technical thinking in the market. IDC also believes that Asia will become the world’s second largest cognitive and artificial intelligence output region in 2020. The article discusses the relationships between robotic process automation and artificial intelligence, and also the impact and changes after implementing the solutions. It has also evaluated and reviewed the effectiveness and following development of the automated solutions, so as to enhance the values of automation solutions and to help companies create optimal advantages in the future challenging and competitive environment.

There were many technical events held in 2017, like 2017 COMPUTEX, Google I/O, Microsoft Design Forum…etc., and Artificial intelligence (AI) is one of the most concerned and hottest topics in these events. There is no doubt that in the next few years, AI technology and its further developments will be the major focus in the science and technology industry. As AI technology brings in a lot of new applications and develops commercial values, for the server industry and corresponding supply chain, it will lead to a new wave and benefit the whole ecosystem. Although the growth rate of desktop and laptop computer slows down and smartphone market starts to saturate, the demand for data processing and computing continues to grow. Large datacenter, server hardware and cloud applications supported at the back-end could still keep their momentum of growth. Taiwan-based original design manufacturers (ODMs) maintain a stable growth rate in 90% of the worldwide server manufacturing and many new business models evolved in order to fulfill the demands brought by artificial intelligence (AI). The aim of this study is to analyze the positive and negative impacts of AI technology on server industry, and will focus on four major groups in the industry: datacenter customers, OEM, ODM and component suppliers.

專家系統為人工智慧發展的一支，至今已廣泛地應用於各個領域，從自然科學到社會 科學不一而足，但在土地登記的領域則尚未發展。法律條文之訂定為人類經驗與智慧 的累積，法官之裁判亦以邏輯推理為基礎，與專家系統的特性不謀而合，因此法律領 域被認為是非常適合發展專家系統的範疇。由於土地登記的相關法令繁瑣，行政命令 更是不計其數，地政人員流動性大，知識的傳遞及累積相當不易，若建立土地登記諮 詢專家系統將可解決這個問題，並且可提供一般民眾查詢填表，輕易地獲取專家的知 識，改善土地行政效率，提高地政機關服務效能，即方便又有效力。 在系統初期發展階段，擬先選取抵押權登記來發展其系統雛型，研究重點在於系統知 識庫的建立。經由問題的確認、知識的萃取表達、控制策略的探討，最後運用專家系 統骨架EXSYS PROFESSIONAL將知識以規則(IF-THEN) 的形式加以組織表達，並以向後 鍵結的方式控制知識庫的推論方向，建構抵押權登記諮詢專家系統之雛型。並兼探討 專家系統在土地登記領域應用之可行性，期能拓展地政電腦化之研究領域，提供後續 研究者參考。 玆將本研究之內容摘要如下： 1 專家系統在土地登記領域應用可行性之探討。 2 抵押權登記之知識表達方法。 3 抵押權登記諮詢專家系統之推理方法與控制策略。 4 抵押權登記諮詢專家系統雛型之建立。 5 未來發展方向之探討。

選舉是實行民主政治最有效的方法之一，而選區劃分的方式將直接或間接的影響投票結果與民主政治理念的施行。 然而在選舉法規或行政區域發生變動時，舊有的選區劃分方式需要隨之調整。而傳統人工的方式具有許多缺點，如：耗費人力資源、人口分配不均、難以兼顧形狀及行政區完整等等。若每次行政區域發生變動，都需要重新劃分，將花費許多不必要的人力、物力及時間，因此利用電腦以完成自動劃分的技術逐漸受到重視。 本論文中我們打破現有的政治與人文鴻溝，嘗試以系統化的方法對選區劃分作全面性的查驗。我們利用計算幾何學的特性與人工智慧搜尋的技巧，儘量找出可能的劃分方式再進行評估。我們依據中選會的建議採用村裡為劃分之最小行政區域，從數以十萬計之合理解中，根據形狀等客觀條件篩選出較佳之劃分方式，進而將歷史投票行為加入考量，以對篩選出的劃分方式作進一步評估與分析。 實作中我們以台南市為對象，在不同的人口限制及形狀條件下，分別比較所能找到的合理解數目。同時選出一部分的劃分方式，和中選會的劃分方式比較，結果顯示我們的方法可以全面性的分析選區劃分，不同的劃分方式可能產生不同的選舉結果。 / Election is one of the most effective way of conducting democratic politics, and mean of electoral redistricting shall post effect, either directly or indirectly, on electoral outcome as well as delivering ideas of democratic politics. As election regulations or administrational districts experience alterations, the present electoral districting is forcefully accompanied with adjustments. Electoral redistricting using traditional human labor works reveal several flaws such as: human resource wastage, uneven population distributions, hard to maintain shape contiguity and compactness, as well as the completeness of administration districts. Every single alteration experience in administration district requires redistribution, thus expensing on unnecessary human labor, resources and time. As such, it had brought great attention on techniques of automatic redistribution by means of modern computer technologies. In this thesis, we shall breakthrough a giant gap between politics and humanity; conduct a thorough examination on systematic approach on electoral redistricting. We are going to utilize characteristics of computational geometry and artificial intelligence searching techniques to find out every conceivable means of redistricting then evaluation the performance of them. By recommendation of Central Election Commission (hence CEM), we will adopt the classification of township as basic unit of administrational district, from counts of thousand adequate explanations, by objective factors of shape accordance and others, select the better means of redistricting methods, and afterward put into concern of historical voting behavior, conduct a further evaluation and analysis upon chosen redistricting method. In actual practices we had selected Tainan City as the experiment target, under different population limitations and factors of form, compare the searchable numbers of decent explanation respectively. We choose some redistricting outcomes, and put into comparison with redistricting method of the CEM. The results indicated our approach is able to conduct a thorough redistricting analysis, as well as more diversified comparing to CEM's outcome. The result of this experiment also reveals different election outcome with adoption of different redistricting methods.

「個案小教授」是一篇探討專家系統方法的研究與應用的探索性論文，主要的應用領域是企管個案教學的輔助教學工具，我們嘗試擴大專家系統的應用的領域，也嘗試去突破一些困難，我們發展出了一個「個案小教授」的雛型。由於專家系統在個案教學上的應用算是首創，如何利用有限的工具來完成千變萬化的個案教學是一大挑戰。本論文將依照知識工程的方法，逐步將個案教學的的精髓融入專家系統的方法中，並以此發現專家系統研究上的一些限制，提供給後續人工智慧與專家系統研究學者參考，使得專家系統能夠跨入更多的領域，幫助人類解決日常決策的問題。本論文採取的研究方法為 　　1.文獻探討：在於整理出發展專家系統的步驟與技術，包括知識擷取方法，知識表現與推理方式，以歸納出知識工程在個案分析教學上應用。 　　2.深入訪談法：知識擷取的工作以知識工程師為界面，透過知識工程師為主導，以交談與口語資料分析(Protocol analysis)等方式將專家知識擷取出來。 　　3.觀察法：利用專家工作的現場與情境實際觀察(使用錄影或是錄音)專家工作方式與推理過程，藉以了解專家知識表現的方式。本研究則是到個案研討的教室實地觀察並記錄司徒達賢教授上課之情形。 　　4.發展系統雛型：專家系統又稱為知識基礎系統(knowledge-based systems)，或知識系統。 　　其系統架構可分為五部份： 　　(1)知識庫(knowledge base)用以儲存專家用以解決問題之知識部份。 　　(2)推理機(inference engine)用以控制推理過程之機制。 　　(3)使用者界面(user interface)用以供使用者友善的解釋及諮詢功能介紹之界面。 　　(4)知識擷取界面(knowledgeacquisition interface)用以提供編輯，增修知識庫之界面。 　　(5)工作記憶區(working memory)用以儲存在推理過程中當時之事實之部份。本研究是以NEURON DATA公司所出品的NEXPERT OBJECT作為系統發展工具，將個案教學專家的知識與推理過程以專家系統加以表現。

資訊科技(Information Technology, IT)對我們的生活與企業帶來極大的影響與改變。在企業中，資訊科技經常扮演不同的角色，這些不同的資訊科技角色(IT Role)可以自動化企業流程、支援決策制定、整合資源，甚至實現轉型與創新，對於企業的決策帶來不同層面的影響。而我們從近年來新興的資訊科技─大數據與人工智慧技術中，發現了不同於過去的新資訊科技角色。為了近一步了解這個新角色，本研究選擇人工智慧應用之一的自動駕駛車作為研究案例。本研究目的是探討自動駕駛車的資訊科技所扮演的新資訊科技角色；研究問題包含 (1) 自動駕駛車的資訊科技如何影響駕駛決策制定 (2) 在決策制定過程中，人與資訊科技分別扮演何種角色與職責。 本研究採用多個案研究法，分為兩個階段。首先，為解構資訊科技的決策制定流程，本研究依據決策理論與系統理論建構一研究架構。於文獻探討的章節中，本研究根據過往文獻與案例，提出四種企業常見的資訊科技角色─Automation、Supporter、Mentor與Enabler，並將研究架構應用於以上資訊科技角色以進行調整與驗證。接著，本研究選擇Google (Waymo)與Tesla作為自動駕駛車的研究個案，並將研究架構套用於兩個個案研發的自動駕駛車。由於不同的自動駕駛車研發理念與實現方式，Google與Tesla自動駕駛車的資訊科技分別扮演兩種不同的資訊科技角色─Autonomer與Smart Automation，本研究進一步比較所有資訊科技角色的研究架構結果，了解資訊科技角色的特性、影響與適用的決策類型。 自動駕駛的決策問題與環境與過去有極大的不同。為了實現安全的自動駕駛，資訊科技需要的資料類型更加多元，除了傳統數位類型資料，也需要收集周遭環境的3D影像等資料；另外，由於決策從過去的靜態問題轉移到動態與快速變化、擁有爆炸性資料與資訊的環境中，資訊科技需要更多的應變能力以制定更即時與適當的決策。由於資料、決策問題與環境的改變，企業對於資訊科技能力的需求也隨之改變，從自動駕駛車的個案中，本研究發現原本的資訊科技角色(Automation、Supporter、Mentor、Enabler)並不具備能應對如此動態與快速變化的決策問題與環境的能力，因而根據個案提出有能力實現動態即時決策制定的兩種新資訊科技角色。 使用人工智慧技術的Google無人駕駛車扮演著Autonomer的角色。資訊科技角色Autonomer能夠與外界進行互動，並且能夠不斷地追蹤、反饋與修正以實現自我成長；此外，面對各種駕駛決策情境，也能夠在無人為干預的情況下獨力完成駕駛決策的制定。資訊科技的學習能力是面對未知與難以預測的問題的最大優勢，而Autonomer的自我學習與決策制定能力也是與其他資訊科技角色最大的不同之處。使用大數據技術的Tesla自動駕駛車的Autopilot系統扮演著Smart Automation。資訊科技角色Smart Automation擁有更進步的資料收集與分析能力，能夠在動態與快速變化的環境中處理更為複雜的決策問題；此外，面對各種駕駛決策情境，Autopilot系統能在駕駛人保持監督的條件下進行自動駕駛以駕駛輔助的方式減輕駕駛人的負擔。最後，我們發現對於決策制定，資訊科技不僅能扮演一個完全獨立的角色，也能夠扮演一個與人互補的角色。大部分的人工智慧如同Google無人駕駛車做為一個Autonomer的角色，但同時更多企業目前使用的資訊科技屬於Supporter、Mentor與Smart Automation以支援或強化決策者的能力。 本研究探討在自動駕駛過程中不同資訊科技角色如何影響決策制定，以及駕駛人與資訊科技的角色與職責。並且從決策類型與資訊科技能力的角度，協助決策者與使用者全面地了解每個資訊科技角色的特性與適用的決策類型。此外，科技不斷在進步，本研究也提供一個了解各種資訊科技角色的基石，透過本研究的研究架構與方法，協助企業與決策者了解不同資訊科技對於決策的影響，本研究結果也能延伸應用於其他自動化、大數據與人工智慧相關領域，如無人工廠、吾人航空載具、工業4.0與金融科技(Fintech)。 / Information technology (IT) has brought great changes to people and business. In various applications, IT plays diverse roles that can automate business processes, support decision-making, integrate resources, and enable transformation and innovation and brings the impacts on different aspect of decision-making in enterprises. However, with the emerging technology of big data and artificial intelligence (AI), there is a new role for IT. To understand this role, we chose the autonomous car, an application of AI, as a study case. The objective of the research is to understand the new roles played by IT in the autonomous car. We focused on two questions: (1) how IT impacts decision-making in the autonomous car; and (2) what roles do IT and humans play during the decision-making process. This study applies a multiple case study in two phases. First, we built a conceptual framework, based on decision theory and system theory, to deconstruct the decision process of IT. To adjust and verify the framework, we applied it to actual cases and proposed IT roles of Automation, Supporter, Mentor and Enabler. Second, we applied the framework to the chosen autonomous car case studies, Google (Waymo) and Tesla, to explore the new role of IT in the autonomous car. Because of the different philosophies, there were two distinct roles played by IT in Google and Tesla’s autonomous cars, Autonomer and Smart Automation, respectively. We furthermore compared the frameworks of Google and Tesla, as well as the existing and new IT roles, explained the differences regarding the IT roles and decision types, and found out the applicable decision-making type of each IT roles.. Compared to the past, there were the great differences for the decision problems and environment of autonomous driving. To realize the safe autonomous driving, the data IT required became more diverse including non-text or non-digit data; besides, the decision-making also changed from static decision problems into dynamic and rapid decision environment with the explosive data and information that IT required more resilience to make decision. Due to the changes of the data, decision problems and environment, the demand for IT capability also changed. From the cases of the autonomous car, we found the original roles including Automation, Supporter, Mentor and Enabler was not enough – they did not possess the capability to make the dynamic and instantaneous decision. Therefore, we proposed two new IT roles – Smart Automation and Autonomer in this research that these two new IT roles which were applicable to the dynamic and instantaneous decision-making. The computer of the Google driverless car using AI technology acted as an Autonomer that was responsible for interacting with the surroundings and being self-growing with continuous tracking and adjustment; furthermore, under driving decision circumstances, this computer could assume the entire decision-making process without human intervention. The self-learning and decision-making ability of Autonomer is the characteristic most different from other IT roles; additionally, the learning ability was the greatest strength for dealing with unknown and unpredictable circumstances. The Autopilot system of the Tesla self-driving car, leveraging big data technology, acted as a Smart Automation that could process more complex decision problems in the dynamic environment with the advancement of data collection and analysis ability; furthermore, under the driving decision circumstances, the Autopilot system of the Tesla self-driving car could temporarily take over the driving control to decrease the driving burden and provide assistance to make driving easier. According to the research results, IT can not only play a totally independent role but also a complementary role. Most AI played the same IT role – Autonomer, such as the computer of the Google driverless car; meanwhile, much of the IT introduced by businesses acted as Supporter, Mentor and Smart Automation to assist and complement humans. This research provided a perspective for identifying how the different IT roles impact decision-making while driving an autonomous car and clarify the responsibility of humans and IT in the driving experience; moreover, from the perspective of decision problems and IT ability, it also provided a comprehensive and general understanding for realizing the characteristics of diverse IT roles and the applicable decision problems.

【中 文 摘 要】 博物館功能主要為「展示、教育、研究、典藏」，其中「展示」為博物館與大眾接觸最直接的方式。在現代科技發展中，「展示」的觀念與形式也有所改變，互動式展示日受重視，虛擬呈現的比例亦逐漸加重。 本研究以「國立海洋生物博物館」之BOT專案「世界水域館」《深海水域》電子展示設計為主題，以實務個案為例，探討自然博物館如何規劃與製作深海水域的生態展演，與如何利用〝虛擬實境〞、〝人工智慧〞與〝即時運算〞等尖端技術，架構出「世界第一」無水水族館的展示模式。 往昔自然生態展示以活體展示為主，即複製生態空間讓水中生物悠遊水族箱內。但為了超越時空，讓全球代表生態皆能集中於特定展示館內，遂有電子展示的觀念與製作，以擬仿物取代真實，創造尚‧布希亞的「超真實」世界。 本研究於製程的影音紀錄與相關人員的訪談中，歸納、整理出發展生態電子展示設計的因素與理想，探討製作上的困難之處，同時也以研究者觀點對展示設計過程提出檢討與建議。 深海水域生態在陸地上展示係屬跨越時空的創舉，本個案不但為台灣首例，在世界上亦為先驅。創新嘗試，成果雖不如預期，但以整個專案的具體呈現而言，實為相關領域之前鋒。本研究認為，整理與探討本個案，對未來電子展示設計與製作皆有參考價值；同時，由本個案所建置的生態電子展示平台，亦為台灣博物館界提供國際化的新思維，對博物館未來的展示設計奠定了新的基礎，創造一個新的開始。 / Abstract The main functions of museums are demonstration/exhibition, education, research and collection/preservation. “Exhibition” provides the most direct link between a museum and the public. As modern science and technology continue to develop, the concepts and formats of “exhibition” have also evolved. Interactive exhibits become more valued, and virtual simulation approaches have also increased in proportion. The focus of this research study is the electronic display design for the Waters of the World – BOT (Build-Operate-Transfer) Project, pioneered by the National Museum of Marine Biology & Aquarium. This study uses this project as a case study to explore how a Nature museum planned and produced an exhibit of The Deep Sea waters ecology, and with the use of the most advanced technology such as VR (Virtual Reality), AI (Artificial Intelligence), and the “real-time operation,” etc., how the museum built the world’s first protocol for a water-less aquarium. In the past, ecological exhibits mainly constituted real living creatures, by duplicating the ecological environment necessary for underwater creatures to survive in an aquarium. But, in order to go beyond the limitation of space and time, and to facilitate the presentation of global ecology within a specific exhibition space, the electronic display concept and production has thus been introduced. It is to imitate reality and create a world of hyper-reality, as depicted by Jean Baudrillard. Relying on historical video and audio records, and actual interviews with key people on the project, this research study summarized factors and objectives of the original design, and discussed difficulties encountered in the production process. In addition, the study also provided input and recommendations concerning the design process. The exhibition of The Deep Sea waters ecology on land is a pioneering act, second to none. The case is not only a 1st in Taiwan, but also a 1st of its kind in the world. Although the new attempt has not exactly achieved the expected outcomes, it did initiate a pioneering work within ecology demo field. The researcher believes that the case study is a valuable reference for future electronic display design and production. In the meantime, the ecological electronic-platform created by this project provides an international perspective, and establishes a new milestone for further development in the future.

近年來，網際網路(Internet)快速發展，已成為一個無疆無界無時差的市場，如何不被這股潮流所淘汰，我們所提出的解決方案—軟體代理人(software agent)，一位具有人工智慧(artificial intelligence)演化調適(adaptive)能力的代理人，現在已經有許多企業與資訊、管理、電腦科學等各方面專家結合，開始使用軟體代理人來代勞，試想一位永不停止、具有創新、學習適應的員工，企業家可以隨意複製或刪除，隨時配合市場規模，不必擔心任何裁員的負擔，這樣的代理人的問世，勢必對我們的經濟環境帶來莫大的衝擊。 電子商務(electronic commerce)已經行之有年，人類的消費型態似乎不易於因這個轉變而有所改變，消費者如果沒有經過視覺、觸覺、嗅覺等感官的刺激，很難有購買的動機，再加上授信制度的不健全使得電子商務的施行充滿了風險。雖然有這麼多問題，我們仍無法阻擋這股趨勢，在電子科技的進步，3D數位影像、各種感官刺激的傳送、或如同期貨市場上明確公認的規格、法律的修訂、完善的認證制度，接下來我們就是要看軟體代理人的表現。 我們將軟體代理人運用在人工雙方喊價(artificial double auction)的市場，就像真實市場已經有人開始使用自動下單或自動議價代理人的機制一樣。然而市場上是否有必然不敗的策略呢？本文就是要解開這個答案。再進一步來看，待真實市場每個成員受不了生存競爭的壓力，也採取使用代理人的演化性策略，屆時我們的人工市場就是真實市場的縮影，我們在本文也會針對這樣一個具有未來前瞻性市場的表現如何？透過經濟學的角度來揭露市場的本質是否仍然維持？ 在本文軟體代理人即為議價代理人(bargaining agent)，她可以在穩定的(stable)市場環境(其他參與者使用固定策略)中辨別出一些有利的市場特徵，藉由這些特徵發展出有利的策略，而其結果甚至不是很容易想到的策略；接著若每個人都使用議價代理人在市場上交易，這裡我們使用一種納許式過程(Nash-like process)來詮釋，之後再分別依市場的分配效率、價格效率、及所得分配來討論市場績效。

本研究的主要目的是希望喚起國內、外學者對演化科學在經濟學上的重視，結合電腦、生物科技、心理學與數學於經濟學中，希望對傳統經濟學上因簡化假設而無法克服的實際經濟問題，可以利用電腦模擬技術獲得解決，並獲取新知與技能。 本研究共有六章，第一章為緒論，敘述緣由與研究動機。第二章介紹傳統經濟學的缺失，再以資料掘取知識及智慧系統建構金融市場。第三章則介紹各種不同人工智慧的方法以模擬金融市場的投資策略。第四章建立無結構性變遷時間序列模型--交易策略電腦模擬分析，僅以遺傳演算法模擬金融市場的投資策略，分別由投資組合、交易成本、調適性、演化、與統計的觀點對策略作績效評分析。第五章則建立簡單的結構性變遷模型，分別由調適性與統計的觀點，採取遺傳演算法再對投資策略進行有效性評估分析。第六章則利用資料掘取知識與智慧系統結合計量經濟學的方法，建構遺傳演算法發展投資策略的步驟，以台灣股票市場的資料進行實証研究，分別就投資策略、交易成本、調適性與演化的觀點作分析。最後一章則為結論。 未來研究的方向有： 1. 其他各種不同人工智慧的方法的比較分析，如人工神經網路、遺傳規劃法等進行績效的交叉比較分析。 2. 利用分類系統(Classifier System)與模糊邏輯的方法，改善標準遺傳演算法對策略編碼的效率，並建構各種不同的複雜策略以符合真實世界的決策過程。 3. 建構其他人工時間資料的模擬比較分析，例如ARCH (Autoregressive Conditional Heteroskedasticity)模型、Threshold 模型、 確定性(Deterministic) 模型等其他時間序列模型與更複雜的結構性變遷模型。 4. 進一步研究遺傳演算法所使用的完整資訊(例如，各種不同指標的選取)。 5. 本研究係採用非即時分析系統(Offline System)，進一步研究即時分析系統 (Online Sysetem)在實務上是有必要的。 / Historically, the study of economics has been advanced by a combination of empirical observation and theoretic development. The analysis of mathematical equilibrium in theoretical economic models has been the predominant mode of progress in recent decades. Such models provide powerful insights into economic processes, but usually make restrictive assumptions and appear to be over simplifications of complex economic system. However, the advent of cheap computing power and new intelligent technologies makes it possible to delve further into some of the complexities inherent in the real economy. It is now feasible to create a rudimentary form of “artificial economic life”. First, we build the framework of artificial stock markets by using data mining and intelligent system. Second, in order to analyze competition among buyers and sellers in the artificial market, we introduce various methods of artificial intelligence to design trading rules, and investigate how machine-learning techniques might be applied to search the optimal investment strategy. Third, we create a miniature economic laboratory to build the artificial stock market by genetic algorithms to analyze investment strategies, by using real and artificial data, which consider both structural change and nonstructural change cases. Finally, we use statistical analysis to examine the performance of the portfolio strategies generated by genetic algorithms.