NDLI: Optimizing player's satisfaction through DDA of game AI by UCT for the Game Dead-End

Content Provider	IEEE Xplore Digital Library
Author	Yidan Zhang Suoju He Junping Wang Yuan Gao Jiajian Yang Xinrui Yu Lindao Sha
Copyright Year	2010
Description	Author affiliation: International School, School of Software Engineering, Beijing University of Posts and Telecommunications, China, 102209 (Yidan Zhang; Suoju He; Junping Wang; Yuan Gao; Jiajian Yang; Xinrui Yu; Lindao Sha)
Abstract	Dealing with players of different skill levels is a key issue for game developers. A major concern for the game developers is to dynamically adjust the difficulty for different players so as to keep them interested in the game. In this paper, we propose “DDA by time-constrained-UCT” to generate intelligent agents to dynamically adapt to the variant capacities of different players. This UCT-based DDA can adjust the game's challenge level by tuning the simulation time of UCT-controlled NPC. However, this approach is not suitable for network game because it consumes a lot storage resource for computation. So we further propose “ANN-from-time-constrained-UCT”, where the data acquired by UCT is applied for training the Artificial Neural Network (ANN) to control the opponents.
Starting Page	4161
Ending Page	4165
File Size	289089
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5583721
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training DDA Fitting Games Artificial neural networks Dogs time-constrained-UCT Polynomials ANN-from-time-constrained-UCT Artificial intelligence
Content Type	Text
Resource Type	Article

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