本論文敘述對於智慧型個人數位相片管理瀏覽平台之研究、設計與實作過程。系統設計上基於整合多重證據架構，採用影像內容與使用者瀏覽行為之分析作為自動分類，判斷影像重要性與推薦程度的依據。影像自動分類方面，包括外部給予的標準資訊-EXIF資訊與分析影像內容，以其中人物存在數量與面積比例為依據的影像分類。而在影像的推薦方面，則採用影像品質之分析-包括對焦品質分析、曝光品質分析-與分析使用者瀏覽相片時的行為-包括停留時間與專注程度的整合為分析重要程度依據；最後則採用多模式(Multi-Modal)架構整合不同的評估結果並作為推薦的結論。 / In this thesis, we present the design and implementation of an intelligent personal digital photo browsing platform. The proposed system relies on multiple evidences inferred from image content as well as user behavior. Specifically, external EXIF data and face detection results are utilized to coarsely classify the digital images. Measures of image quality, including clarity and contrast, are calculated to further refine the search result. Moreover, we use web cameras to record and analyze the viewing behavior of the user and attempt to correlate the interest of the viewer to the effective viewing time. Finally, a multi-modal system is put in place to integrate the clues acquired from different modules.

近年來，智慧型互動電視的應用是不少數位生活空間研究的焦點之一。我們認為，好的互動式數位電視系統必須有生動靈活的使用介面與使用者互動。在本研究中，我們提出以智慧型3D動畫角色為介面之互動數位電視系統，希望可以藉由智慧型3D動畫角色在介面上的呈現，加強使用者於互動數位電視系統的使用經驗。在我們過去所開發的互動數位電視系統SITV上，有許多不同的互動情境可納入智慧型3D動畫角色設計的考量。我們提議讓智慧型3D動畫角色在肢體動作表現上具有行動力與表達力的概念，使得動畫角色在互動數位電視系統上，能夠依據不同的情境與角色本身之狀態，選擇適當的動作在螢幕上移動並且能呈現適當的情緒，讓互動數位電視系統之服務更加友善。本研究以JAVA開發動畫系統，並設計實驗驗證不同介面對使用者的影響，結果顯示，使用者認為智慧型3D動畫角色介面是最有善的。 / In recent years, intelligent interactive digital TV is one of the most important applications in the research of digital living space. We think a good interactive TV system must have a vivid user interface to interact with users. In this research, we propose to incorporate intelligent 3D character into the interface design for interactive digital TV system, to enhance the user experience of the interactive digital TV system. In the smart interactive digital TV system we developed before, call SITV, many interactive scenarios can be considered in the design of intelligent 3D character. We propose to develop our intelligent 3D character with the concepts of mobility and expressiveness on body motion such that appropriate emotions can be presented through motions depending on the scenario and character configuration. For example, an intelligent 3D character can act like a housekeeper living in the TV monitor. He can take different actions for different scenarios to make the service friendlier. We have developed our animation system in JAVA and designed experiments to evaluate different types of user interface design on different scenarios. The experimental results show that an interface with an intelligent 3D character will be friendlier than the others.

在機器學習與深度學習技術帶動第三次人工智慧熱潮，特別是與機器人、大數據、3D列印等結合，「人工智慧」成為各大科技企業重點發展技術，無論是透過成立研究小組或是併購的方式，在2016年即有40多家人工智慧技術公司被併購，同時這些技術也被應用在各式產品與服務中。此外不同產業中也陸續引進人工智慧，從事需要耗時費力的基礎工作，節省成本，也引發人類被取代的恐慌。世界各國除著力投資發展人工智慧之外，也重視人工智慧為社會及經濟產生的影響與現行法制的衝擊。 人工智慧技術也應用於創作與發明過程中，且在機器學習技術下，人類僅需輸入指示與限制，人工智慧完成內容創作所產出作品，與人類創作成果並無二致。或是像是神燈精靈一般，人類只要以人工智慧可理解的方式定義問題、要件分析、功能設計，人工智慧即能完成最重要的物理設計，解決人類之問題，其產出物可能是符合產業利用性、新穎性與進步性等專利要件。這些人工智慧創作與發明物是否符合我國現行著作權法與專利法之規定而受到保護，將是本文探討重點，本文將以文獻研究以及比較法的方式深入研究。 智慧財產權制度設立的主旨是為保護人類精神活動成果，以人類為創作主體為前提，人工智慧參與創作之成果對於現行制度而言自然有所扞格。然而人工智慧創作力對於未來創作與發明而言，都是有所助益，可豐富文化的多樣性並加快技術的發展。而日本知識產權戰略本部也於2016年四月時也將針對人工智慧創作物之法律保護，擬修訂智慧財產權法。如我國未來亦研擬將人工智慧創作物納入法律保護，本文參考日本立法相關討論以及美國學者之見解，提出立法時應考量的權利歸屬以及衍生的相關問題。 / Machine Learning and Deep Learning are leading the new artificial intelligence era, especially when integrated with technologies of robot, big data, and 3D printing. As A.I. gradually became the one of the most popular technologies, corporate giants, such as Google, IBM, Facebook, and Apple, have been setting up research labs and acquiring A.I. startups to improve the quality of their services and products. Meanwhile, through using their service and products, our daily life is filled with A.I. Moreover, in many different industries, companies are using A.I. to reduce their cost by replacing labors from time-consuming jobs. Governments not only invest in the development of A.I. technology, but also response to the impact A.I. brings to the society, economic and Law. Artificial creativity is a new way for creation and invention. With machine learning, human only need to input the indication and limitation for A.I. to generate the outcome which is almost the same as what human can do. A.I. is also being described as a “genie in the machine”. Human input the description of their problems, functional analysis, and functional design, then A.I. will do the physical design to solve the problems and generate inventions which are useful, novelty and un-obviousness. Whether these creations and inventions are copyrightable and patentable is the core of this essay. Intellectual property system is aimed to protect the result from human’s mind activity, so the author or inventor must be human beings. When A.I. is not just a creation tool, but a creative subject, that’s where a conflict occurs. However, A.I. creativity is beneficial to human creative and inventing activity, because it can quicken the progress of technologies and improve culture diversity. Legislators in Japan are planning to protect A.I. creation through modification of intellectual property law. If we also expect to protect A.I. creation and invention in Taiwan in the future, with Japan legislative discussion and America scholars’ theories, this essay might offer some useful indications on the right attribution and other derivation problems.

運用語義網本體論來描述網路服務，實現網路服務的自動發現、調用和組合已經被證實是有效的。人工智慧規劃技術就是運用此技術描述網路服務來達到自動化網路服務組合。OWL-S支援使用OWL來描述網路服務的前提與效果，而在OWL的規則描述語言方面，則可以使用SWRL。本論文是以OntoComposer規劃工具為基礎，發展一套前置處理器來簡化其使用，讓使用者不需具備人工智慧規劃描述語言及語義網路服務描述等知識，只要尋找到符合需求之網路服務後，就可以自動轉換成支持條件分支圖規劃器之輸入文件，並在設定目標後進行規劃組合，最後讓組合之複合網路能夠在執行引擎上正確執行。 / Using Semantic Web ontologies to describe Web Services has proven to be effective for automatic service discovery, invokcation and composition. AI planning techniques have been employed to automate the composition of Web Services in this way. OWL-S supports the description of the preconditions and effects of a web service using OWL statements, and SWRL is the language for expressing OWL Rules. OntoComposer is an AI planning based tool for Combining-GraphPlan, an extension for GraphPlan so that supports condition branching. This thesis presents a preprocessor for OntoComposer to simplify its input task so that the user does not have to learn the AI Planning description language and knowledge of the semantic web service description. Just look to meet the demand of web services, our preprocessor will translate them to support branch planning for the input file. After setting the targets the OntoComposer will compose some component web services to a complex web service. Finally, let the composition of web services on the execution engine the correct execution.

各類電子商務網站上的推薦機制應用已日趨廣泛且成熟。而隨著決策問題日漸複雜，現行的推薦機制發展已經可以看到應用的界限，再也無法貼近使用者所面臨的複雜問題。現行的推薦機制架構需要被重新審視、定義與設計其核心演算法。本研究用更寬廣的角度看待推薦機制，並將改良後的推薦機制視為解決問題的新典範。 首先，本研究定義了改良後的推薦機制所應支援的功能，包括：階層式條件的多維度推薦以及多階段的推薦。多維度推薦機制能夠讓使用者從不同的面向去看待決策問題，而階層式條件則允許使用者針對每個維度再往下設定階層式條件，幫助決策者更貼切的描述所遭遇的問題，如此一來推薦機制所提供的推論結果才能更符合決策者的原意。而多階段推薦則是協助決策者進行一連串的規劃方案，而這樣的推薦結論能夠提供可行方案的遠景，讓決策者能夠預先為可能發生的狀況進行準備，進而深化決策者對目前推薦結論的信心。 除了力求每個(或多個)階段推薦結論的正確性，推薦系統也要與所有的決策階段緊密結合（不僅止於資料搜集階段），所以必須能夠提供決策者行為面的建議，確切的建議決策者應該採取的行動。確切的行為面資訊推薦結論對於決策活動的參考價值更高。 所以，本研究修改了傳統的案例推導法(CBR)，試圖讓傳統CBR演算法成為符合改良後個案推薦機制的規範，因為CBR演算法最符合人類求解問題的邏輯程序，因此本研究在改良式個案推薦機制中重現CBR演算法中的4R推理循環。而且為了真正落實修正後的CBR演算法，本研究還結合了基因演算法提出GCBR的概念，幫助改良式個案推薦系統能夠更快速有效的收斂出推薦的結論。 最後，本研究也預期所提出的推薦機制能夠應用於各種不同的領域，而為了驗證所提出的推薦機制執行效率與可行性，本研究也列舉了數個實驗進行的規範方案。本研究所提出的改良式個案推薦機制核心演算法為一概化模型，能夠求解不同型態的決策問題。 / Recommender system can be regarded as fundamental technology of electronic commence web site. Some researchers also claimed that recommender system push the electronic web site to another development peak. Recommender system would need some mechanisms. These recommender mechanisms should be reviewed, redefined and expanded to include particularly case-based mechanism that focus on reality problem solving. Recently, CBR applications had been extended to provide recommendation mechanism based on previous cases. The abstract recommendation problems are usually hard to be formulated in strict mathematic models, and often solved via word-mouse experience. Case-Based Reasoning (CBR) is a paradigm, concept and instinctive mechanism for ill-defined and unstructured problem solving. Similarly to human problem solving process, CBR retrieves past experiences to reuse for target problem. Of course, the solutions of past cases may need to be revised for applying. The successful problem-solving experiences are then retained for further reusing. These are well-known 4R processes (retrieve, reuse, revise, and retain) of traditional CBR. Nevertheless, the case-based recommender mechanism is particularly suitable for reality problem reference because case-style can be used to describe unstructured problem. The next generation recommender mechanism should focus on the real life problem solving and applications. Thus, case-based recommender mechanism can be regarded as a new problem solving paradigm. To enhance traditional CBR algorithm to case-based recommender mechanism, the original CBR should be redesigned. In the traditional CBR algorithm, based on multiple objectives, the retrieved cases could provide to decision maker for references. However, as the decision problem is getting complex, pure multiple objective problem representation is too unsophisticated to reflect reality. Thus, a revised CBR algorithm equipped with capability to deal with more complexity is needed. Additionally, decision makers would wish to achieve the actionable information. The existing recommender mechanism can not provide the actionable direction to decision maker. Based on previous cases provided by CBR, decision maker would further hope that recommender mechanism could tell them how to do. These capabilities should be included into traditional CBR algorithm. Furthermore, traditional CBR has to evaluate all cases in case base to return the most similar case(s). The efficiency of CBR is obviously negatively related to the size of case base. Thus, a number of approaches have devoted to decrease the effort for case evaluation. This research proposes a revised CBR mechanism, named GCBR, which can be regarded as next generation CBR algorithm. GCBR can be applied to reality applications, particularly case-based recommender mechanism. Thus, it can be treated as a new problem solving paradigm. It also intends to improve traditional CBR efficiency stability no matter what kinds of case representation and indexing approaches.

司法院經常聘請資深的法官將民事裁判中具有參考價值的法律意見摘錄出來，製作成民事裁判要旨，民事裁判要旨可作為法官審理類似案件時的辦案參考，因此，在司法實務上民事裁判的搜尋為不可或缺的工作。然隨著資訊科技的發達及裁判數量的累積，民裁判要旨的搜尋結果可能多達數百篇，造成法官須耗費大量的時間在民事裁判要旨的閱讀上，如果能利用資料探勘的技術將搜尋到的民事裁判要旨加以分群，且分群的正確性又可達到一定旳水準，便可節省法官閱讀民事裁判要旨的時間。在本研究中我們嘗試將資料探勘技術中的階層式分群法應用在民事裁判要旨的分群上，並將法律條文所出現的用語作為加權的主關鍵字評估可否改善分群的效果，以探討資料探勘技術中的階層式分群法應用在民事裁判要旨分群上的可行性與成效。 / Judicial Yuan often invites senior civil judges to extract legal opinions from civil judgments for making the purports of civil judgments. The purports of civil judgments can be consulted as trial judges handle the similar cases, therefore, in judicial practices, it is an indispensable work for civil judges to search the purports of civil judgments. However, with the development of information technology and the cumulative number of judgments, the number of search results may be as high as hundreds, civil judges must have spent a lot of time reviewing of the purports of civil judgments. If we can utilize data mining technologies to cluster the search results, and the accuracy of clustering can be attained to a certain standard, it will save civil judges a lot of time on reviewing the purports of civil judgments. In this study we attempt to apply hierarchical method on the clustering of the purports of civil judgments, and adjust the weights of main keywords derived from frequently used vocabulary of legal provisions to assess the feasibility and effectiveness of application of hierarchical method on clustering of the purports of civil judgments.

樂齡商機因人口老化問題而持續受到關注，新聞媒體更是屢屢報導各國各界對樂齡市場龐大商機的預估，惟與現實生活對比後不難發現樂齡商品或服務甚為不足；故而在自身與年邁父母對樂齡服務的需求，觀察時下樂齡人口及其家庭可能面臨的問題與需求，便興起「樂齡服務平台」的念頭。 在客觀驗證目標族群樣貌後，因應多元而分散的特性，以及現今尚未成型的樂齡市場環境下，「樂齡服務平台」的商業模式必將面對足以開疆闢土、滿足需求的核心競爭力建構問題。是而考量樂齡族群的需求特色及痛點外，亦檢視個人的專業、經驗與人脈、資源範圍，遂鎖定可同時滿足多方條件考量的「AI ChatBot聊天機器人」科技運用，成為「樂齡人工智慧服務平台」構想起源。 由於現今台灣對於人工智慧「AI ChatBot聊天機器人」技術甚為陌生，再加上技術功能範疇複雜，故將藉由本篇論文探討研究的過程，鎖定此人工智慧平台之初步策略，客觀分析樂齡需求痛點，對照研究所得之「AI ChatBot聊天機器人」功能特色與運用限制是否得以滿足，並檢視平台本身建置「AI ChatBot聊天機器人」的能力是否足夠面對競爭，進而評估「樂齡人工智慧服務平台」核心競爭力與發展初步策略的適切性及完整度。

本研究結合由Holland(1975)所發展的遺傳程式與資訊科技的計算理論來研究臺灣股票市場的市場擇時策略，並根據Bauer(1994)對於投資法則的編碼方式，分別考慮以基本分析與技術分析為基礎的遺傳程式市場擇時策略，並且以買入持有策略、投資無風險資產與追漲殺跌策略等三種投資策略作為比較基礎來評估遺傳程式在投資策略應用上的可行性。 　　研究期間為1984年至1991年，其中樣本內期間為1984年至1988年，而樣本外期間為1989年至1991年，研究結論如下： 　　1.不論採基本分析或技術分析為基礎的遺傳程式投資法則，在樣本外期間第一年（1989年）的報酬均顯著高於投資無險資產與追漲殺跌策略，但低於買入持有策略。 　　2.就整個樣本外期間（1989-91年）而言，採用基本分析的遺傳程式投資法則顯著優於買入持有策略，而採用技術分析的遺傳程式投資法則並不具有投資績效。 　　3.以基本分析為基礎的遺傳程式投資法則較適用於長期投資，而以技術分析為基礎的遺傳程式投資法則較適用於短期投資。 　　4.樣本外期間經歷了遺傳程式沒有學習過的資料型態（亦即1990年初股價連續下跌趨勢），對於遺傳程式的學習能力形成了一大挑戰。

每股盈餘是公司的重要財務資訊之一，它可以反應公司的經營績效，因此一方面可以提供給投資者作為投資決策之參考，另一方面提供給管理者作為管理評量的參考指標之一。過去在每股盈餘等財務預測往往以統計方法進行，因此在自變數選擇上常受到限制，同時有些預測模式其輸出結果往往只能以常長或衰退等二元式的結果表示。而另一方面，以類神經網路預測方式的預測模式可能因變數增加，使得網路變的較複雜。本研究嘗試以人工智慧中的遺傳演算法來作為預測的工具，發展財務預測模型，來預測每股盈餘，解決過去預測方式的限制或缺點。同時也將對過去的遺傳演算法稍做修正，並嘗試以實際值的編碼方式進行編碼，以符合需求。最後進一步比較遺傳演算法和其他預測方式，瞭解以遺傳演算法做於預測每股盈餘工具的特性及優缺點。 / Earnings per share (EPS) is one of the important financial indicators to a corporation. It reflects the operating performance of a corporation. On one hand, EPS provides information available to investors for decision making; on the other hand, it is an indexfor measurement of management. In the past, financial forecasting was often done by using statistical models. However, the input variables were limited by using these statistical models. Besides, some stastical models only provide dichotomy output ,such as either "grwoth" or"decline". The neural network forecasting model will be more of complexity, when the input variable increases. This research attempts to develop a financial forecasting model to forecast the EPS by using the Genetic Algorithms, which is a new topic of artificial intelligence. This model excludes both the limitations and disadvantages of the models mentioned above. Here, the genetic algorithms will be modified and the real number will be used to code as a gene of achromosome to meet the requirements of the finacial model. Finally,we compare the genetic algorithms financial forecasting model with the other ones in order to understand the features, advantages and disadvantages of genetic algorithms as being a financial forecasting tool .

近年來由於國內經濟之持續成長，證券市場的交易也日益活絡，因此藉著在證券市場發行證券的方式來籌措資金，以便從事大規模經營的公司家數也越來越多。隨著股票上市申請案件的增加，證交所上市部負責股票上市案件審查人員的升遷、調職及離職，精通審查程序的證券專家便相對的稀少。為加速申請案件之審查、保留證券專家的工作經驗與知識、及訓練新進人員，發展專家系統是值得一試的措施。 在人工智慧領域中，專家系統技術已成為由實驗理論進入商品化的一種技術，為人工智慧技術中較為成熟的一環。國外已經有不少成功的應用專家系統實例，國內各單位目前對於人工智慧的研究，也多半集中在專家系統的開發上。然而，擷取領域專家的知識及經驗，並由這些知識及經驗架構而成的知識庫，是專家系統的主體。因此，本研究目的有二：一、收集相關資料，整理出在股票上市案件審查程序上的知識與經驗，並以恰當的知識表示法轉換成電腦程式。二、利用GoldWorks Ⅲ在個人電腦上設計一個專家系統的雛型系。 本研究已經運用專家系統建構工具GoldWorks Ⅲ建構完成輔助股票上市案件審查的專家系統雛型，本雛型系統包含了兩大子系統：(1)分類子系統，(2)審查準則子系統。經由本研究之探討及系統實際設計，可得以下結論：ぇ經系統評估的結果發現，將專家系統之觀念與技術應用在輔助股票上市案件審查過程上是可行的；え將股票上市案件審查所需的專業知識及經驗予以形式化後，並以規則基礎知識表示法設計成知識庫，保存在電腦系統中。因此，如果審查人員都在系統的引導之下作出合理的判斷，當能提高股票上市案件審查判斷結果的一致性。(3)透過本研究專家系統雛型之執行及解釋，使得經過適當訓練的非專家，亦能享受專家的知識及經驗，亦可藉以輔助股票上市案件審查專業人才的培訓。(4)使用專家系統建構工具設計系統雛 型，能快速的在短期間內建立一個雛型系統，應該是未來應用的主流。