Adaptive Strategy Game Engine Using Non-monotonic Reasoning and Inductive Machine Learning 


Vol. 11,  No. 1, pp. 83-90, Feb.  2004
10.3745/KIPSTB.2004.11.1.83


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  Abstract

Strategic games are missing special qualities of genre these dats. Game engines neither reson about behaviors of computer objects nor have learning ability that can prepare countermeasure in variously command user's strategy. This paper suggests a strategic game engine that applies non-monotonic reasoning and inductive machine learning. The engine emphasizes three components - 'user behavior monitor' to abstract user's objects behavior, 'learning engine' to learn user's strategy, 'behavior display handler' to reflect abstracted behavior of computer objects on game. Especially, this paper poposes two layerd-structure to apply non-monotonic reasoning and inductive learning to make behaviors of strategies of computer onjects with created information through inductive learning. Main contribution of this paper is that computer objects command excellent strategies and reveal differentiation with behavior of existing computer objects to apply non-monitornic reasoning and inductive machine learning.

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[IEEE Style]

K. J. Min and P. Y. Taeg, "Adaptive Strategy Game Engine Using Non-monotonic Reasoning and Inductive Machine Learning," The KIPS Transactions:PartB , vol. 11, no. 1, pp. 83-90, 2004. DOI: 10.3745/KIPSTB.2004.11.1.83.

[ACM Style]

Kim Je Min and Park Yeong Taeg. 2004. Adaptive Strategy Game Engine Using Non-monotonic Reasoning and Inductive Machine Learning. The KIPS Transactions:PartB , 11, 1, (2004), 83-90. DOI: 10.3745/KIPSTB.2004.11.1.83.

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