請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66606
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 許永真(Jane Yung-jen Hsu) | |
dc.contributor.author | Yen-Ling Kuo | en |
dc.contributor.author | 郭彥伶 | zh_TW |
dc.date.accessioned | 2021-06-17T00:45:55Z | - |
dc.date.available | 2012-02-08 | |
dc.date.copyright | 2012-02-08 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-01-03 | |
dc.identifier.citation | [1] E. Cambria, A. Hussain, C. Havasi, and C. Eckl. AffectiveSpace: Blending common sense and affective knowledge to perform emotive reasoning. In Proceedings of the CAEPIA Workshop on Opinion Mining and Sentiment Analysis, 2009.
[2] A. Carlson, J. Betteridge, R. C. Wang, E. R. Hruschka Jr., and T. M. Mitchell. Coupled semi-supervised learning for information extraction. In Proceedings of the Third ACM International Conference on Web Search and Data Mining, 2010. [3] T. Chklovski. Learner: a system for acquiring commonsense knowledge by analogy. In K-CAP 03: Proceedings of the 2nd International Conference on Knowledge Capture, 2003. [4] T. Chklovski and Y. Gil. An analysis of knowledge collected from volunteer contributors. In Proceedings of the Twentieth National Conference on Arti cial Intelligence (AAAI-05), 2005. [5] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391–407, 1990. [6] C. Eckart and G. Young. The approximation of one matrix by another of lower rank. Psychometrika, 1(3):211–218, 1936. [7] I. Eslick. Searching for commonsense. Master’s thesis, Massachusetts Institute of Technology, 2006. [8] O. Etzioni, M. Cafarella, D. Downey, A.-M. Popescu, T. Shaked, S. Soderland, D. S. Weld, and A. Yates. Methods for domain-independent information extraction from the web: an experimental comparison. In Proceedings of the 19th national conference on Arti cal intelligence, 2004. [9] J. Euzenat and P. Shvaiko. Ontology matching. Springer-Verlag, Heidelberg (DE), 2007. [10] C. Havasi, J. Pustejovsky, R. Speer, and H. Lieberman. Digital intuition: Applying common sense using dimensionality reduction. IEEE Intelligent Systems, 24(4):24–35, July 2009. [11] C. Havasi, R. Speer, and J. Alonso. ConceptNet 3: A flexible, multilingual semantic network for common sense knowledge. In Recent Advances in Natural Language Processing, Borovets, Bulgaria, September 2007. [12] C. Havasi, R. Speer, and J. Pustejovsky. Coarse word-sense disambiguation using common sense. In 2010 AAAI Fall Symposium on Common Sense Knowledge (FSS10), 2010. [13] Y.-L. Kuo and J. Y.-j. Hsu. Bridging common sense knowledge bases with analogy by graph similarity. In 2010 AAAI Workshop on Collaboratively-Built Knowledge Sources and Arti ficial Intelligence. AAAI Press, July 2010. [14] Y.-L. Kuo and J. Y.-j. Hsu. Goal-oriented knowledge collection. In 2010 AAAI Fall Symposium on Common Sense Knowledge (FSS10), 2010. [15] Y. L. Kuo, J. C. Lee, K. Y. Chiang, R. Wang, E. Shen, C. W. Chan, and J. Y.-j. Hsu. Community-based game design: experiments on social games for commonsense data collection. In Proceedings of the ACM SIGKDD Workshop on Human Computation, 2009. [16] D. B. Lenat. CYC: A large-scale investment in knowledge infrastructure. Communications of the ACM, 38(11):33–38, 1995. [17] H. Lieberman. User interface goals, AI opportunities. AI Magazine, 30:16–23, 2009. [18] H. Lieberman, H. Liu, P. Singh, and B. Barry. Beating common sense into interactive applications. AI Magazine, 25:63–76, 2004. [19] H. Liu, H. Lieberman, and T. Selker. GOOSE: a goal-oriented search engine with commonsense. In Adaptive Hypermedia and Adaptive Web-Based Systems, Second International Conference, AH 2002, 2002. [20] H. Liu and P. Singh. ConceptNet: A practical commonsense reasoning toolkit. BT Technology Journal, 22(4):211–226, 2004. [21] D. Martin, M. Burstein, E. Hobbs, O. Lassila, D. Mcdermott, S. Mcilraith, S. Narayanan, B. Parsia, T. Payne, E. Sirin, N. Srinivasan, and K. Sycara. OWL-S: Semantic Markup for Web Services. Technical report, 2004. [22] D. L. McGuinness, R. Fikes, J. Rice, and S. Wilder. The chimaera ontology environment. In Proceedings of the Seventeenth National Conference on Arti ficial Intelligence and Twelfth Conference on Innovative Applications of Arti ficial Intelligence, 2000. [23] S. McIlraith and T. C. Son. Adapting golog for composition of semantic web services. In Proceedings of the 8th International Conference on Knowledge Representation and Reasoning, Toulouse, France, April 2002. [24] B. Medjahed, A. Bouguettaya, and A. K. Elmagarmid. Composing web services on the semantic web. The VLDB Journel, 12(4):43–54, November 2003. [25] G. A. Miller. WordNet: A lexical database for English. Communications of the ACM, 38(11):39–41, 1995. [26] M. Minsky. The Society of Mind. Simon and Schuster, 1988. [27] A. Newell and G. Ernst. The search for generality. In Proceedings of IFIP Congress, 1965. [28] M. Nodine, J. Fowler, T. Ksiezyk, B. Perry, M. Taylor, and A. Unruh. Active information gathering in InfoSleuth. International Journal of Cooperative Information Systems, 9(1/2):3–28, 2000. [29] N. F. Noy and M. A. Musen. Prompt: Algorithm and tool for automated ontology merging and alignment. In Proceedings of the Seventeenth National Conference on Artifi cial Intelligence and Twelfth Conference on Innovative Applications of Arti ficial Intelligence, 2000. [30] Organization for the Advancement of Structured Information Standards (OASIS). Web Services Business Process Execution Language (WS-BPEL) Version 2.0, 2007. [31] K. Panton, C. Matuszek, D. Lenat, D. Schneider, M. Witbrock, N. Siegel, and B. Shepard. Common sense reasoning – from Cyc to intelligent assistant. Lecture Notes in Computer Science, 3864:1–31, 2006. [32] T. Pedersen, S. Patwardhan, and J. Michelizzi. WordNet::Similarity - measuring the relatedness of concepts. In Proceedings of the 19th National Conference on Artifi cial Intelligence (AAAI-04), 2004. [33] A. Preece, K. Hui, A. Gray, T. Bench-capon, D. Joes, and Z. Cui. The KRAFT architecture for knowledge fusion and transformation. In Proceedings of the 19th SGES International Conference on Knowledge-Based Systems and Applied Artifi cial Intelligence, 1999. [34] J. Rao, P. Kungas, and M. Matskin. Logic-based web services composition: from service description to process model. In Proceedings of the 2004 International Conference on Web Services, pages 446 – 453, July 2004. [35] J. Rao and X. Su. A Survey of Automated Web Service Composition Methods. Semantic Web Services and Web Process Composition, 3387:43–54, 2005. [36] L. Schubert and M. Tong. Extracting and evaluating general world knowledge from the brown corpus. In Proceedings of the HLT-NAACL Workshop on Text Meaning, 2003. [37] E. Y.-T. Shen, H. Lieberman, and G. Davenport. What’s next?: emergent storytelling from video collection. In Proceedings of the 27th International Conference on Human Factors in Computing Systems, CHI ’09, pages 809–818, New York, NY, USA, 2009. ACM. [38] M. P. Singh and M. N. Huhns. Service-Oriented Computing: Semantics, Processes, Agents. Wiley, 2005. [39] P. Singh. The public acquisition of commonsense knowledge. In Proceedings of AAAI Spring Symposium, 2002. [40] P. Singh, T. Lin, E. T. Mueller, G. Lim, T. Perkins, and W. L. Zhu. Open mind common sense: Knowledge acquisition from the general public. In On the Move to Meaningful Internet Systems, 2002 - DOA/Coop IS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002, 2002. [41] E. Sirin, B. Parsia, D. Wu, J. Hendler, and D. Nau. Htn planning for web service composition using shop2. Web Semantics: Science, Services and Agents on the World Wide Web, 1(4):377 – 396, 2004. [42] A. Smirnov, M. Pashkin, N. Chilov, and T. Levashova. Multi-agent architecture for knowledge fusion from distributed sources. Lecture Notes in Arti ficial Intelligence, 9(2296):293–302, 2002. [43] R. Speer, C. Havasi, and H. Lieberman. AnalogySpace: Reducing the dimensionality of common sense knowledge. In Proceedings of AAAI-2008, 2008. [44] M. Strube and S. P. Ponzetto. WikiRelate! computing semantic relatedness using wikipedia. In Proceedings of the 21st National Conference on Artifi cial Intelligence (AAAI-06), 2006. [45] G. Stumme and A. Maedche. Fca-merge: Bottom-up merging of ontologies. In Prceedings of the 7th International Conference on Arti ficial Intelligence, 2001. [46] L. von Ahn, M. Kedia, and M. Blum. Verbosity: A game for collecting commonsense knowledge. In ACM Conference on Human Factors in Computing Systems (CHI Notes), pages 75–78, 2006. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66606 | - |
dc.description.abstract | 智慧系統需要常識知識使其更能應付使用者的各種狀況,雖然現今已建立了許多大型的常識知識庫,但這些常識知識庫也都還不完整。應用程式在使用常識知識時,往往因為知識庫選擇的知識表現與推理方式而被侷限於只能使用單一的知識庫做應用。相對於將所有知識庫合併成單一知識庫的作法,本論文提出一個用於常識知識整合之多代理人系統,以及系統中的三個重要機制:(1) 配對含有目標知識的知識庫以做推理 (2) 結合多個推理方法來回答應用程式的查詢 (3) 在有限的人力配置下以群眾外包的技術收集知識以增進知識庫的覆蓋率。最後,本論文以影片編輯與對話輔助的使用者介面作為應用案例,由此兩個案例證實結合此多代理人推理系統的介面代理人可以顯著地增加其處理使用者需求的數量。 | zh_TW |
dc.description.abstract | Robust intelligent systems require commonsense knowledge. While significant progress has been made in building large commonsense knowledge bases, they are intrinsically incomplete. It is difficult to combine multiple knowledge bases due to their different choices of representations and reasoning techniques, thereby limiting users to one knowledge base and its reasoning methods for any specific task. Instead of merging knowledge bases into a single one, this paper presents a multiagent system for commonsense knowledge integration, and proposes approaches to (1) matching knowledge bases without a common ontology for reasoning, (2) combining different reasoning methods to answer queries from application, and (3) improving coverage of knowledge base via resource-bounded crowdsourcing. Two case studies on video editing and dialog assistance interfaces are also presented to show their improvement in handling user’s actions after incorporating the proposed reasoning system. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T00:45:55Z (GMT). No. of bitstreams: 1 ntu-101-R98922037-1.pdf: 8044470 bytes, checksum: 7582f6c822bd2965c1adf34bd36c585e (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Intelligent User Interface Requires Common Sense . . . . . . . 2 1.1.2 Challenges in Using Common Sense . . . . . . . . . . . . . . . 4 1.2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.2 Commonsense Knowledge Integration Problem . . . . . . . . . 6 1.3 Proposed Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Chapter 2 Background 10 2.1 Commonsense Computing . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.1 Knowledge Representation . . . . . . . . . . . . . . . . . . . . 11 2.1.2 Commonsense Knowledge Collection . . . . . . . . . . . . . . 11 2.1.3 Commonsense Reasoning . . . . . . . . . . . . . . . . . . . . . 13 2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.1 Knowledge Source Integration . . . . . . . . . . . . . . . . . . 16 2.2.2 Web Service Composition . . . . . . . . . . . . . . . . . . . . 17 Chapter 3 Commonsense Knowledge Integration 19 3.1 Multiagent Framework . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.1 Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.1.2 Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2 Matchmaking of Reasoning Agents . . . . . . . . . . . . . . . . . . . 23 3.2.1 Capability Modeling . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.2 Capability Evaluation for Matchmaking . . . . . . . . . . . . . 25 3.3 Reasoning Composition . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.1 Profile of Reasoning Method . . . . . . . . . . . . . . . . . . . 28 3.3.2 Composition Algorithm . . . . . . . . . . . . . . . . . . . . . . 31 3.4 Improving Coverage of KB by Crowd-sourcing . . . . . . . . . . . . . 33 3.4.1 Resource-bounded Knowledge Acquisition . . . . . . . . . . . 34 3.4.2 Guiding KB . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.4.3 Similarity as a Weak Inference . . . . . . . . . . . . . . . . . . 38 3.4.4 Acquisition via KB Approximation . . . . . . . . . . . . . . . 40 Chapter 4 Experimental Design and Result 43 4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.1.1 Reasoning Method . . . . . . . . . . . . . . . . . . . . . . . . 44 4.1.2 Planner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.2 Matchmaking of Reasoning Agents . . . . . . . . . . . . . . . . . . . 45 4.2.1 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . 45 4.2.2 Experimental Result . . . . . . . . . . . . . . . . . . . . . . . 46 4.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.3 Reasoning Composition . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3.1 Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.3.2 Correctness of Reasoning Results . . . . . . . . . . . . . . . . 51 4.4 Improving Coverage of KB . . . . . . . . . . . . . . . . . . . . . . . . 53 4.4.1 Acquisition method: Virtual Pets . . . . . . . . . . . . . . . . 53 4.4.2 Experimental Result . . . . . . . . . . . . . . . . . . . . . . . 54 Chapter 5 Case Study 59 5.1 Study 1: Storied Navigation . . . . . . . . . . . . . . . . . . . . . . . 59 5.2 Study 2: Dialog Assistant . . . . . . . . . . . . . . . . . . . . . . . . 61 Chapter 6 Conclusion 66 6.1 Summary of Contribution . . . . . . . . . . . . . . . . . . . . . . . . 66 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 Bibliography 69 | |
dc.language.iso | en | |
dc.title | 多代理人推理系統於整合常識知識之研究 | zh_TW |
dc.title | A Multiagent Reasoning System for Commonsense Knowledge Integration | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃乾綱,蔡宗翰,陳伶志 | |
dc.subject.keyword | 常識運算,多代理人系統,常識知識整合,常識知識,常識推理,群眾外包,介面代理人, | zh_TW |
dc.subject.keyword | commonsense computing,multiagent system,commonsense knowledge integration,commonsense knowledge,commonsense reasoning,crowdsourcing,interface agent, | en |
dc.relation.page | 74 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-01-04 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-101-1.pdf 目前未授權公開取用 | 7.86 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。