Abstract
Soft computing is extensively used in the field of computer games to create AI agents for computers. A case study of reinforcement learning is presented, by designing an AI agent for chopsticks game, with a probabilistic algorithm devised to make use of past game experience as its only tool to guide itself to victory. It has been experimentally verified that the AI agent’s performance increases with learning and nears saturation beyond a point of learning. Constant order space and time complexity is achieved with proper design of knowledge base.
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Jayashree, P., Ramakrishnan, K. (2018). Design and Evaluation of Reinforcement Learning Based AI Agent: A Case Study in Gaming. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_33
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DOI: https://doi.org/10.1007/978-3-319-60618-7_33
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