Abstract
This paper presents the intelligent game agent that gives effective intelligence to NPCs (Non Player Characters) for which intelligence did not exist. Generally, non-player characters (NPCs), or agents, such as monsters, enemy guards, or friendly wingmen, can be controlled by a finite-state machine. To overcome the shortcoming of NPC’s restricted action, we applied a LSVM (Linear Support Vector Machine) as pattern recognition for the intelligent game agent, and processed all data in XML format to handle the data efficiently. The intelligent agent is executed in the base of the game physics engine. A lot of NPCs that act in a game learn physics values that are produced in the game, and change NPS’s action intelligently. We applied two pattern recognition algorithms to estimate the algorithm’s performance through comparison. As indicated by experiments, when the M-BP has a fixed number of input layers (number of physical parameters) and output layers (impact value), it shows the best performance when the number of hidden layers is 3 and the learning count number is 30,000. The pattern recognizer that applied LSVM shows the best performance when the learning count number is 25000, and the LSVM shows better performance than the M-BP in the intelligent game agent.
This study was supported by a grant of the Seoul R&BD Program.
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Choi, J., shin, D., Shin, D. (2006). Intelligent Game Agent Based Physics Engine for Intelligent Non Player Characters. In: Shi, ZZ., Sadananda, R. (eds) Agent Computing and Multi-Agent Systems. PRIMA 2006. Lecture Notes in Computer Science(), vol 4088. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11802372_42
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DOI: https://doi.org/10.1007/11802372_42
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