Please use this identifier to cite or link to this item:
Title: Adaptive game AI for gomoku
Authors: Tan, K.L.
Tan, C.H.
Tan, K.C. 
Tay, A. 
Keywords: Adaptive
Player satisfaction.
Issue Date: 2009
Citation: Tan, K.L.,Tan, C.H.,Tan, K.C.,Tay, A. (2009). Adaptive game AI for gomoku. ICARA 2009 - Proceedings of the 4th International Conference on Autonomous Robots and Agents : 507-512. ScholarBank@NUS Repository.
Abstract: The field of game intelligence has seen an increase in player centric research. That is, machine learning techniques are employed in games with the objective of providing an entertaining and satisfying game experience for the human player. This paper proposes an adaptive game AI that can scale its level of difficulty according to the human player's level of capability for the game freestyle Gomoku. The proposed algorithm scales the level of difficulty during the game and between games based on how well the human player is performing such that it will not be too easy or too difficult. The adaptive game AI was sent out to 50 human respondents as feasibility. It was observed that the adaptive AI was able to successfully scale the level of difficulty to match that of the human player, and the human player found it enjoyable playing at a level similar to his/her own. ©2009 IEEE.
Source Title: ICARA 2009 - Proceedings of the 4th International Conference on Autonomous Robots and Agents
ISBN: 9781424427130
DOI: 10.1109/ICARA.2000.4804026
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Apr 11, 2021

Page view(s)

checked on Apr 12, 2021

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.