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研究生:吳俸昌
研究生(外文):Feng-Chang Wu
論文名稱:在時間限制下的個人化旅遊行程規劃
論文名稱(外文):personal trip planning with time constraints
指導教授:蘇豐文蘇豐文引用關係
指導教授(外文):Von-Wun Soo
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:56
中文關鍵詞:個人化旅遊行程規劃時間限制
外文關鍵詞:personal trip planningtime constraints
相關次數:
  • 被引用被引用:6
  • 點閱點閱:2143
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:7
當使用者在規劃旅遊行程的時候,他們必須先要蒐集尋找相關的旅遊資訊,他們想要知道有哪些選擇並且希望挑出其中最好的選擇。如果使用者是去傳統的旅行社的話,旅行社的人會根據使用者的經驗以及他們的旅遊相關知識給予使用者意見以及幫助。因此當我們在設計一個旅遊規劃系統的時候,我們必須要知道如何將使用者的喜好模組化。每個使用者在許多方面都是不一樣的,比方說使用者的狀態,過去旅遊的經驗以及特殊的喜好等等,這些在我們規劃旅遊行程的時候都必須要考慮進來以增進我們規劃出來的旅遊行程的品質。除此之外,參觀景點本身也有自己的限制,像是開門時間,關門時間以及花費等等。旅遊規劃系統不只除了必須滿足使用者的喜好與特殊需求外,還得確保其行程能夠滿足許多空間的、時間的、外在環境的限制,例如:住宿旅館是否已客滿等等。
本篇論文中,我們最感興趣的是旅遊代理人如何以對話與協商的方式來推薦旅遊行程,最後產生出一個任人滿意的結果出來。除此之外我們還利用案例式推理的方式來規劃路徑。還要利用使用者過去的旅遊經驗來推薦旅遊景點,最後還要討論如何解決使用者的要求和地點的時間限制發生衝突的問題。
Abstract
When planning a trip, people are searching for information. They want to know what choices they might have and they must evaluate these choices in order to choose the best of them. If they are going to a traditional travel agency, the agent will give them advises, based on personal knowledge or other customers’ experience. Therefore when we develop a trip planning system, we must know how to model user’s preference. As users differ in many parameters, like status, expertise and preferences, all these should be taken into account to improve the quality of trip planning. Visiting spots also have many constraints such as opening time, closing time and cost. The trip planning system has to satisfy not only the customer’s personal preferences and constraint, but also the complex spatial, temporal, physical constraints and cost imposed by the transportation methods among visiting spots and various environmental supports such as the availability of hotels.
In this thesis, we are in particular interested in understanding how a travel agent could conduct such kinds of recommendation and negotiation dialogues with the user and come out with a satisfactory trip plan for the user. We use case-based reasoning to plan personal route. Besides, we will recommend visiting spots for users according to users’ experiences. We will focus on how to resolve of constraint violation when there are conflicts between user’s preferences and visiting spots’ time constraints.
目錄
摘要 1
Abstract 2
目錄 3
第一章 序論 4
1.1 研究動機 5
1.2 研究目標 6
1.3 系統架構 7
1.4 論文架構 8
第二章 相關研究工作探討 9
2.1 什麼是案例式推理 9
2.2 什麼是智慧型代理人 9
2.3 FIPA代理人管理關係模型 9
2.4 多代理人系統 11
2.5 Jade 12
第三章 系統架構與實做方法 15
3.1 整體系統架構 15
3.2 Tourism Ontology 17
3.3 推薦旅遊地點 21
3.3.1 相似度和推薦度計算 22
3.4 建立初步旅遊計畫 31
3.4.1 建立旅遊景點安排順序 31
3.4.2 調整旅遊景點停留時間 35
3.5 路徑規劃代理人 40
3.5.1 最短路徑演算法 40
3.5.2 個人化路徑 43
第四章 實驗結果 45
第五章 結論以及未來展望 53
參考文獻 55
參考文獻
[1] Bellifemine F., Poggi, A. and Rimassa, G. (1999) JADE — A FIPA-compliant agent framework, in Proceedings of PAAM''99, London, April 1999, pagg.97-108.
[2] FIPA Org, http://www.fipa.org/
[3] FIPA Interaction Protocol Library Specification.
See http://www.fipa.org/specs/fipa00025/XC00025D.pdf
[4] FIPA Agent Communication Language Specification.
See http://www.fipa.org/specs/fipa00003/OC000003.pdf
[5] Stuart Russel and Peter Norvig, Artificial Intelligence — A Modern Approach, Pretice Hall, 1995.
[6] FIPA 97 Draft Specification Part 4 (1997) Personal Travel Assistance, 1997 FIPA - Foundation for Intelligent Physical Agents.
[7] Ndumu, D. T, Collis, J.C. & Nwana, H. S.(1998) Towards Desktop Personal Travel Agents. BT Technology Journal, 16(3) 1998, 69-78.
[8] K.Z. Haigh, J.R. Shewchuk, and M. Veloso (1994) Route Planning and Learning form execution. AAAI Press, November, 1994, pp. 58-64.
[9] Genesereth, M. R., and Ketchpel, S. P. (1994) Software Agents, Communications of ACM, 37, NO 7, pp 48-53.
[10] Maes, P. (1994) Agents that Reduce Work and Information Overload. Comm. ACM 37(17), 30-40. Special Issues on Intelligent Agents.
[11] Liu, B., et al. (1994). Integrating case-based reasoning, knowledge-based approach and Dijkstra algorithm for route finding. In, Proceedings of the Conference on Artificial Intelligence Applications, pp.149-155
[12] J.L. Kolodner, An Introduction to Case-Based Reasoning, Artificial Intelligence Review 6, 1992, PP 3-34
[13] Mark Claypool Anuja Gokhale, Tim Miranda, Pavel Murnikov, Dmitry Netes and Matthew Sartin, Combining Content-based and Collaborative Filtering in an On-Line Newspaper, ACM SIGIR Workshop on Recommender Systems Berkeley, CA, 1999
[14] Balabanovic, M., and Shoham, Y. (1997) Combining Content-Based and Collaborative Recommendation. Communication of the ACM, March 1997.
[15] Fox, M. S. (1987) Constraint-Directed Search: A Case Study of Job-Shop Scheduling. San Mateo, Calif.: Morgan Kaufmann.
[16] Vipin Kumar (1992), Algorithms for Constraint Satisfaction Problems — A Survey, AI Magazine 13(1):32-44, 1992
[17] Fox, M. S., Sadah, N., and Baykan, C. (1989) Constraint Heuristic Search. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, 309-315. Menlo Park, Calif.: International Joint Conference on Artificial Intelligence.
[18] Parsons, Simon and Jennings, N. R., Negotiation through Argumentation — A Preliminary Report, Proceedings of the second International conference on Multi-Agent Systems, 1996.
[19] Maes, P. (1994) Agents that Reduce Work and Information Overload. Comm. ACM 37(17), 30-40. Special Issues on Intelligent Agents.
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