臺灣博碩士論文加值系統

In this paper, an intelligent game engine framework called GENI is proposed to recognize, analyze, and predict player actions on-line. First, the martial art styles are recorded using the Kinect device, then we utilize the Dynamic Time Wrapping (DTW) algorithm to analysis and recognize real-time user input style. Second, after learning the model, we collect the logs of the on-line recognized behaviors data, extract interesting patterns, and obtain reliable probabilities from the mining step. Two efficient approaches namely MSSBE and MSSMB are proposed for mining the behavior data and finding the interesting style patterns. In addition, a suffix matching algorithm is proposed to discover the proper style sequences of an avatar for predicting future responses of opponents. The N-Gram predictor algorithm is also presented for evaluating the efficiency of our methods. Finally, to demonstrate how those algorithms apply to real game scenarios, we implement the framework using a scene graph based approach with rich graphical user interfaces to fulfill the game experiment environment.

Moreover, we also apply GENI in walkthrough research and propose several new approaches based on a behavioral oriented system that uses traversal patterns to model relationships between users and exploits semantic-based clustering techniques, such as association, intra-relationships, and inter-relationships, to explore additional links throughout the walkthrough system.

I. INTRODUCTION 1
II. RELATED WORK 6
2.1 Game Engine Framework and Technology 6
2.2 Motion Recognition and Analysis 9
2.3 Opponent Modeling and Action Prediction 19
2.4 Walkthrough 26
III. GENI ARCHITECTURE 39
3.1 Main Conceptual Model 39
3.2 System and Functional Diagrams 40
3.3 Scene Graph based Game Engine Framework 41
IV. MOTION RECOGNITION AND ANALYSIS 48
V. ACTION PREDICTION 56
5.1 Data Stream Mining based Algorithms 56
5.2 N-Gram based Algorithms 68
5.2.1 N-Gram Predictor 68
5.2.2 Suffix Matching Predictor 71
VI. APPLICATION IN WALKTHROUGH 79
6.1 Utilizing Traveral Sequence Order for Storage Layout 79
VII. SUMMARY AND CONCLUSIONS 86
REFERENCES 88

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