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.

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