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研究生: 許弘霖
Hsu, Hung-Lin
論文名稱: ContextSense-環境智能的常識知識庫系統
ContextSense–A Commonsense Knowledge Base System for Ambient Intelligence
指導教授: 鄭泰昇
Jeng, Tay-Sheng
學位類別: 碩士
Master
系所名稱: 規劃與設計學院 - 建築學系
Department of Architecture
論文出版年: 2006
畢業學年度: 94
語文別: 中文
論文頁數: 85
中文關鍵詞: 情境感知常識知識環境智能智慧空間
外文關鍵詞: Context Sensing, Ambient Intelligence, Smart Space, Commonsense Knowledge
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  • 近年來許多研究人員和工業界投入許多心力在發展感應器架構的技術,將其置入到環境智能(ambient intelligence)中,創造可以感應、適應、反應人活動的數位化環境[philips],通常所採取的策略是發展由嵌入到空間的智慧感應器(smart sensor),來建制可程式的空間,同時開發互動的系統可以主動的回應使用者的需求。這些感知特徵的背後動機是要擴增人的能力、影響人的經驗、認知,如此可以重新定義人機互動的領域,然而,許多的研究成果都強調感應器的能力[Benford et al, 2005],較少關注在建制關於人日常生活經驗的邏輯架構[Benford et al, 2005],近日逐漸成形的共識、最可行的方法是建構常識知識(commonsense)介面、建構人類經驗的模型在環境智能中,此常識包含空間、物理、社交、時序關係、心理等關於生活的各種面向[Minsky, 2000]。
    本論文將介紹事件常識知識(Event Commonsense)的架構可以有效的在『環境智能』(Ambient intelligence)環境中捕捉人的經驗,認為透過有關生活常識的再現,建立規格化的邏輯架構可以有效瞭解人複雜的經驗。
    為什麼不容易把人工智慧應用到環境智能,有四個主要的原因,第一,封閉世界的假設,因為我們處在一個開放的世界,一個簡單的事實是電腦可以打敗最高段的棋士,卻不足以處理現實世界理簡單的生活機能,第二,缺乏基本常識,電腦不能掌握普通生活,問題不在於研發複雜的人工智慧計算能力與推理能力,真正的問題是電腦不知道人類的生活,第三,單一知識的呈現,人類的知識涉及各式各樣浩大、複雜的知識,無法使用單一的知識呈現方法來表現各樣的知識,第四,不允許嘗試錯誤,電腦設定的邏輯與規則不允許嘗試錯誤,但是人總是喜歡反覆嘗試找出自己的喜好。
    為了解決這些問題,我們提出一個事件常識知識的模型和平台,在三個條件下解決這些問題,第一,建構開放式架構,聚集眾人無盡的生活知識,第二,建構生活的基本常識,包含事件常識知識的模型和知識的推演,第三,具備擴充性,來面對生活的複雜度和人性不同的適應程度。

    The new millennium has seen that researchers and industries have developing sensing-based technologies to situate an ambient intelligence environment that is sensitive, adaptive, and responsive to the presence of people [Philips]. An often used strategy is to develop a programmable space by embedding smart sensors into building products and developing interactive applications that proactively respond to human needs. The main motivation behind these sentient features is the impact on human experience that could redefine the scope of human-computer interaction. However, many research efforts have emphasized sensing capabilities [Benford et al, 2005], with less concern for a formalized logical framework of human experience underlying everyday life or formal underpinnings [Abowd, Mynatt, and Rodden, 2002]. A growing consensus is that the most desirable way to support ambient intelligence is to construct commonsense interfaces with a model of human experience, encompassing knowledge about the spatial, physical, social, temporal, and psychological aspects of everyday life [Minsky, 2000].
    This paper presents a semantic network of commonsense knowledge that is useful in capturing human experience in ambient intelligence environments. We argue that the complexity of human experience can be resolved by modeling interaction in a formalized logical framework through the representation (e.g. predicate logics) of commonsense knowledge about everyday life.
    There are four reasons about why it is hard to deploy artificial intelligence to ambient intelligence. The first reason is Closed-world assumption, because we live in an open ended and dynamic world. The obvious fact is that computer can beat the high-graded chess player but can’t deal with the simple function of daily life. The second reason is lack of commonsense. The problem of using computers to deal with daily life is not the computing or reasoning capability. It lack of commonsense which a five years old boy can easily handle. The third is single knowledge representation which can not include human’s complex knowledge. The final is no tolerance of trial and error, because humans used to try and sense to check what is preferred.
    In order to solve this problem we propose a commonsense model and platform confronting in three issues. The first criterion is to build an Open Framework to collect human experience of daily life. The second is to construct Commonsense knowledge base for everyday living, including knowledge modeling and reasoning. The final is that the application should possess extensibility and adaptability to deal with the complexity of everyday life and the adaptability of human beings.

    第1章 緒論 1 1-1 研究背景 2 1-2 研究問題 4 1-3 研究目的 7 1-4 論文架構 8 第2章 文獻回顧 9 2-1 文獻回顧 10 2-2 比較分析 14 2-3 可應用領域 15 第3章 行為感知與行為模型 17 3-1 感測技術 18 3-2 行為觀察與分析 19 3-3 事件常識模型 27 第4章 系統設計 29 4-1 開放式知識分享事件常識模型與推理機制 32 4-2 研發開放式事件常識資料庫 37 4-3 串聯環境周遭的人機介面感應裝置 41 第5章 系統實作與評估 45 5-1 系統實作 46 5-2 使用觀察與系統測試 51 第6章 結論與未來發展 55 6-1 結論 56 6-2 未來發展 57

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