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Title: Creating autonomous adaptive agents in a real-time first-person shooter computer game
Authors: Wang, Di
Tan, Ah Hwee
Keywords: Adaptive Resonance Theory Operations
Real-time Computer Game
DRNTU::Engineering::Computer science and engineering
Issue Date: 2015
Source: Wang, D., & Tan, A. H. (2015). Creating autonomous adaptive agents in a real-time first-person shooter computer game. IEEE Transactions on Computational Intelligence and AI in Games, 7(2), 123-138. doi:10.1109/TCIAIG.2014.2336702
Series/Report no.: IEEE Transactions on Computational Intelligence and AI in Games
Abstract: Games are good test-beds to evaluate AI methodologies. In recent years, there has been a vast amount of research dealing with real-time computer games other than the traditional board games or card games. This paper illustrates how we create agents by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first-person shooter computer game called Unreal Tournament. Rewards used for learning are either obtained from the game environment or estimated using the temporal difference learning scheme. In this way, the agents are able to acquire proper strategies and discover the effectiveness of different weapons without any guidance or intervention. The experimental results show that our agents learn effectively and appropriately from scratch while playing the game in real-time. Moreover, with the previously learned knowledge retained, our agent is able to adapt to a different opponent in a different map within a relatively short period of time.
ISSN: 1943-068X
DOI: 10.1109/TCIAIG.2014.2336702
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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