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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.

客戶關係管理（Customer Relationship Management, CRM）藉由各種資訊技術來留住客戶，以產生更多的商業價值。然而，許多文獻指出，CRM系統的失敗率很高，尤其是CRM主要的核心能力--銷售員自動化（Sales Force Automation, SFA）。研究指出改善的方式包含更好的管理支援、培訓、系統易用性和強烈的使用動機等等。接續此建議，本文提出了一個銷售員支援（Sales Force Support, SFS）系統，藉由線上分析處理（Online Analytical Processing, OLAP）、資料採礦（Data Mining, DM）和雲端服務（Cloud Service）等技術，協助彙整及提供支援銷售員的客戶推薦 (Customer Recommendation)和自我績效評估(Self Evaluation)功能，以刺激更好的銷售能力、滿足客戶與管理。可望提高系統的易用性和業務人員的使用動機，藉以橋接銷售員和管理人員之間的差異。為了評估推薦功能之適用性，本論文也發展一套驗證指標，並採用一套隨機數學模型（Stochastic Mathematical Model），作為強化推薦預測之嘗試。 / Customer Relationship Management (CRM) adopts various information technologies to retain and attain customers in order to generate more business values. However, the earlier studies indicate the failure rate for CRM systems is high and it’s even higher for Sales Force Automation (SFA), a major core in CRM. They usually suggest the enhancement in better management support, more training, user friendliness, and usage motivation, and so on. Following the suggestions, this research proposes a Sales Force Support (SFS) system to integrate technologies like OLAP (Online Analytical Processing), Data Mining (DM), and cloud service, etc. to provide supporting information in customer recommendation and self-evaluation, in order to better stimulate sales and satisfy customer and management. The objectives can be achieved by enhancing the user friendliness and usage motivation, and bridging the differences between sales force and management. To evaluate the fitness of recommendation function, a set of validation measures is also developed. In addition, a stochastic mathematical model is also attempted to enhance the recommendation prediction.

由於網際網路的普及造成資訊量愈來愈大，在資訊的搜尋、整理與閱讀上會耗費許多時間，因此本研究提出一應用文本主題及關係探勘的方法，將多份文件自動生成一篇摘要，以幫助使用者能降低資訊的閱讀時間，並能快速理解文件所欲表達之意涵。 本研究以電影評論文章為例，結合文章結構的概念，將影評摘要分為「電影資訊」、「電影劇情介紹」及「心得結論」三部分，其中「電影資訊」及「心得結論」為透過本研究建置之電影領域相關詞庫比對得出。接著將餘下之段落歸屬於「電影劇情介紹」，並透過LDA主題模型將段落分群，再運用主題關係地圖的概念挑選各群之代表段落並排序，最後將各段落去除連接詞及將代名詞還原為其所指之主詞，以形成一篇列點式影評摘要。 研究結果顯示，本研究所實驗之三部電影，產生之摘要能涵蓋較多的資訊內容，提升了摘要之多樣性，在與最佳範本摘要的相似度比對上，分別提升了10.8228%、14.0123%及25.8142%，可知本研究方法能有效掌握文件之重點內容，生成之摘要更為全面，藉由此方法讓使用者自動彙整電影評論文章，以生成一精簡之摘要，幫助使用者節省其在資訊的搜尋及閱讀的時間，以便能快速了解相關電影之資訊及評論。 / The rapid development of information technology over the past decades has dramatically increased the amount of online information. Because of the time-wasting on absorbing large amounts of information for users, we would like to present a method in this thesis by using text topic and relationship mining for multi-document summarization to help users grasp the theme of multiple documents quickly and easily by reading the accurate summary without reading the whole documents. We use movie reviews as an example of multi-document summarization and apply the concept of article structures to categorize summary into film data, film orientation and conclusion by comparing the thesaurus of movie review field built by this thesis. Then we cluster the paragraphs in the structure of film orientation into different topics by Latent Dirichlet Allocation (LDA). Next, we apply the concept of text relationship map, a network of paragraphs and the node in the network referring to a paragraph and an edge indicating that the corresponding paragraphs are related to each other, to extract the most important paragraph in each topic and order them. Finally, we remove conjunctions and replace pronouns with the name it indicates in each extracted paragraph s and generate a bullet-point summary. From the result, the summary produced by this thesis can cover different topics of contents and improve the diversity of the summary. The similarities compared with the produced summaries and the best-sample summaries raise of 10.8228%, 14.0123% and 25.8142% respectively. The method presented in this thesis grasps the key contents effectively and generates a comprehensive summary. By providing this method, we try to let users aggregate the movie reviews automatically and generate a simplified summary to help them reduce the time in searching and reading articles.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22094号 / 情博第704号 / 新制||情||121(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 熊田 孝恒, 教授 黒橋 禎夫, 教授 楠見 孝, 准教授 河原 大輔 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

京都大学 / 0048 / 新制・論文博士 / 博士(経済学) / 乙第13059号 / 論経博第394号 / 新制||経||280(附属図書館) / 立命館大学大学院経営学研究科 / (主査)教授 塩地 洋, 教授 椙山 泰生, 准教授 田中 彰 / 学位規則第4条第2項該当 / Doctor of Economics / Kyoto University / DGAM

本研究は,双腕で作業プレートを支持することで閉リンク機構を構成し,作業プレートを操ることで十分な支持剛性を維持しながらプレート上での柔軟な作業空間を実現することを目指した.すなわち,ロボットでも従来の工作機械のNC制御の運動精度に近い性能を具現化する一方,人の作業に近い柔軟性を両立することであるその対象としてプレート上で球の転がりを取り上げ,その運動軌跡をコンピュータシミュレーションにより解析しながら,同時2軸の旋回運動の制御におけるその誤差解析およびその改善手法を確立する. / Recently, industrial dual-arm robots have gained attention as novel tools and we therefore focus on them to flexible control both the linear motion and the rotational motion of a working plate. However, it is difficult to measure the synchronous accuracy of two rotary axes without high accuracy gyro sensor. Thus, we proposed a novel method to measure it with a ball, which keeps a ball rolling around a circular path on it by dual-arm cooperative control. As a result, we demonstrated this method made it feasible to estimate the synchronous accuracy of two rotary axes of working plate. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

In the nearest future, the human driver is viewed as a reliable backup even for the fully automated road motor vehicles (cars). Indeed, the driver is assumed to swiftly take the control of the car in cases of suddenly occurring (i) challenging environmental conditions, (ii) complex unforeseen driving situations, or (iii) degradation of performance of the car. However, due to the cognitive overload in such a sudden, stressful takeover of the control, the driver would often experience the startle effect, which usually results in an unconscious, instinctive, yet incorrect response. An extreme case of startle is freezing, in which the driver might be incapable to respond to the sudden takeover of control at all. The possible approaches to alleviate the startle during the takeover of control (i.e., the automation startle) include an offset- (i.e., either early- or delayed-), gradual yielding the controls to the driver. In the cases considered above, however, these approaches are hardly applicable because of (i) the presumed unpredictability of the events that result in the need of takeover of control, and (ii) the severe time constraints of the latter. Conversely, the objective of our research is to propose an approach of minimizing the need of yielding the control to the driver in challenging environmental conditions by guaranteeing an adequate automated control in these conditions. Focusing on slippery roads as an instance of challenging conditions, and steering control as an instance of control, we aim at developing such an automated steering that controls the car adequately in various road surfaces featuring low friction coefficients without the need of driver’s intervention.In order to develop such an automated steering we employed an in-house evolutionary computation framework – XML-based genetic programming (XGP) – which offers a flexible, portable, and human readable representation of the evolved optimal steering functions. The trial runs of the evolved steering functions were performed in the Open Source Racing Car Simulator (TORCS), which features a realistic, yet computationally efficient simulation of the car and its environment. The obtained experimental results indicate that due to the challenging dynamics of the unstable car on slippery roads, neither the canonical (tuned) servo-control (as a variant of PD) nor the (tuned) PID-controller could control the car adequately on slippery roads. On the other hand, the controller, featuring a relaxed, arbitrary structure evolved by XGP outperforms both the servo- and PID controllers in that it results in a minimal deviation of the car from its intended trajectory in rainy, snowy, and icy road conditions. Moreover, the evolved steering that employs anticipated perceptions is even superior as it could anticipate the imminent understeering of the car at the entry of the turns and consequently – to compensate for such an understeering by proactively turning the steering wheels in advance – well before entering the turn. The obtained results suggest a human competitiveness of the evolved automated steering as it outperforms the commonly used alternative steering controllers proposed by human experts. The research could be viewed as a step towards the evolutionary development of automated steering of cars in challenging environmental conditions. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

本研究では，油圧ショベルの効率の良い自動掘削を目的として，提案した自動掘削アルゴリズムを，個別要素法を用いた土壌モデルと油圧ショベルの動力学モデルを組みわせたシミュレーションモデルによって検証する．さらに，シミュレーションの実機検証を目的として，油圧で駆動する模型を用いて，縮小モデル実験機を構築し，提案した自動掘削アルゴリズムの実機検証実験を行う． / In this study, for the purpose of efficiency automatic digging of hydraulic excavators, the proposed automatic digging algorithm is verified by the simulation model that combines a soil model using the Distinct Element Method and a dynamic model of the hydraulic excavator. Furthermore, for the purpose of verifying by the actual excavator, a scale model experimental machine is constructed using a hydraulically operated model excavator, and the actual machine verification experiments of the proposed automatic digging algorithm are conducted. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

科学研究費補助金 研究種目:基盤研究(C) 課題番号:07630022 研究代表者:木下 宗七 研究期間:1995-1996年度