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研究生:許育誠
研究生(外文):Yu-Cheng Hsu
論文名稱:在多代理人虛擬情境中敵對策略之反制
論文名稱(外文):Countering Adversarial Strategies in Multi-Agent Virtual Scenarios
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:2006
畢業學年度:94
語文別:英文
論文頁數:38
中文關鍵詞:虛擬代理人敵對策略虛擬情境
外文關鍵詞:Virtual AgentAdversary StrategyVirtual ScenarioHTNCounterplanning
相關次數:
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  • 下載下載:16
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近年來擬真代理人逐漸在人工智慧領域中被廣泛應用於智慧型教育系統(Intelligent Tutoring Systems)、虛擬情境及數位娛樂中,使用者藉由與擬真代理人互動達到教育以及娛樂效果。傳統上,擬真代理人專注於與合作或友善之使用者互動,代理人主要扮演著輔助者的角色,然而當虛擬代理人應用於軍事或戲劇等敵對場景時,將面對不合作甚至帶有攻擊性之代理人,若能有效反制敵對策略,可進一步使虛擬代理人產生更為真實的互動行為。目前虛擬代理人著重於如何成功猜測敵對策略及成功反制敵對策略,然而面對一個狡黠、聰明使用者或代理人而言,對方同樣具有猜測及反制之能力,在這種敵對策略情境中,若虛擬代理人能成功有效反制敵對策略,不僅可展現更為擬人之行為互動模式,更可進一步應用於自動故事產生或軍事領域上。
在本論文中,主要提出一個完整的多回合敵對策略反制架構,傳統上敵對策略反制著重於如何找出最佳反制策略,本研究進一步延伸至當面對敵人反制策略時,代理人如何更進一步找出更好之反制策略,進一步擴展至多回合反制架構。本篇論文主要以HTN(Hierarchical Task Network)以及機率方式找出最佳反制策略,並建立一個完整敵對策略產生流程圖。
Mutual modeling and plotting between virtual characters is an important issue in scenarios with interactive multi-agent dramas and games. To exhibit such interactions, virtual agents in adversarial situations are required to counter the actions of others and prevent their actions from being countered. This thesis provides a framework for agents to model the intentions of others and to select the optimal strategy by simulating multi-level mutual modeling using a hierarchical plan structure based on the AND-OR tree. The resulting plan considers multiple rounds of counter-strategy and possible adversarial reactions, creating deeper strategic interaction. A brief scenario illustrates our approach.
中文摘要 II
ABSTRACT III
ACKNOWLEDGE IV
CHAPTER 1 INTRODUCTION - 1 -
1.1 MOTIVATION - 2 -
1.2 THESIS ORGANIZATION - 3 -
CHAPTER 2 RELATIVE WORK - 4 -
2.1 MODELING OTHER AGENTS - 4 -
2.1.1 Model of Behavior vs. Model of Mind - 4 -
2.2 COUNTERPLANNING AND ADVERSARIAL PLANNING - 5 -
2.2.1 Multi-Level Modeling - 6 -
CHAPTER 3 A FRAMEWORK FOR MULTI-LEVEL MODELING - 9 -
3.3 REPRESENTATION OF INTENTIONS OF SELF AND THE OPPONENT AGENTS - 10 -
3.4 PROPAGATING THE PROBABILITY OF SUCCESS - 12 -
3.5 PROPAGATING THE EXECUTION TIME - 13 -
3.6 COUNTERING ONE-LEVEL ADVERSARIAL STRATEGY - 14 -
3.6.1 Evaluate the Candidate Counterplans - 14 -
3.6.2 Executing the Counterplans - 16 -
3.7 MULTI-LEVEL MODELING AND COUNTERING - 18 -
3.8 EXECUTION FLOW - 21 -
CHAPTER 4 SCENARIO ILLUSTRATION - 23 -
4.1 DESCRIBE THE MACBETH SCENARIO - 23 -
4.2 APPLY MULTI-LEVEL COUNTER APPROACH TO MACBETH SCENARIO - 23 -
4.2.1 First-level Simulation - 24 -
4.2.2 Second-level Simulation - 25 -
4.2.3 Third-level Simulation - 25 -
4.3 ANALYSIS OF SIMULATION SCENARIO - 27 -
CHAPTER 5 EXPERIMENTAL EVALUATION - 28 -
5.1 GENERAL INFORMATION - 28 -
5.2 COUNTERING ADVERSARY IN ONE-LEVEL - 29 -
5.3 MULTI-LEVEL COUNTERING - 31 -
5.4 ARBITRARY LEVEL COUNTERING - 32 -
5.5 OVERALL EXPERIMENTAL SUMMARY - 34 -
CHAPTER 6 CONCLUSION AND FUTURE WORK - 35 -
REFERENCE - 36 -
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10. Kaminka, G., Fidanboylu, M., Chang, A., Veloso, M.: Learning the Sequential Coordinated Behavior of Teams from Observation. In: Kaminka, G., Lima, P., Rojas, R. (eds): RoboCup 2002: Robot Soccer World Cup VI. Lecture Notes in Computer Science, Vol. 2752. Springer-Verlag, Berlin Heidelberg New York (2003) 111-125
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