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Lookup NU author(s): Dr Haoran Duan
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IEEEThe evolution of natural life is guided by a perpetually adaptive set of rules, encompassing natural laws, human policies, and game mechanics. Automated game design, through the creation of simulated environments populated by AI agents, embodies these rules, aligning with the objectives of artificial life research that seeks to replicate the dynamics of biological life through computational models. This paper presents a comprehensive framework, the Rule Generation Networks (RGN), devised for automated rule design, evaluation, and evolution in line with controllable expectations. We refine and formalize three cardinal elements - rules, strategies, and evaluation - to elucidate the intricate relationships inherent in rule generation tasks. The RGN integrates generative neural networks for rule design and a suite of reinforcement learning models for rule evaluation. To exemplify rule evolution and adaptation across varying environments, we introduce a controllability metric to gauge game dynamics and evolve the rule designer accordingly. Furthermore, we develop two game environments, Maze Run and Trust Evolution, modelling human exploration and societal trade dynamics, to gamify and evaluate the generated rules.
Author(s): Pu J, Duan H, Zhao J, Long Y
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024
Volume: 34
Issue: 8
Pages: 6874-6887
Print publication date: 01/08/2024
Online publication date: 20/11/2023
Acceptance date: 02/04/2023
ISSN (print): 1051-8215
ISSN (electronic): 1558-2205
Publisher: Institute of Electrical and Electronics Engineers Inc.
URL: https://doi.org/10.1109/TCSVT.2023.3334526
DOI: 10.1109/TCSVT.2023.3334526
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