Abstract
From DeepBlue to AlphaGo, computer game is the drosophila of Artificial Intelligence. For the AI services of assisting human learning, we believe computer board games can also play the role of the drosophila. From the viewpoint of social development, after the rise of AI, human need more ability of logical thinking and judgement than before. Advocating computer games is an excellent tool for the training of logical concepts and hence produces positive impact in our society. Since human Go players have a different process of reasoning compared to Go programs today, we need to develop learning methods that more closely match how humans think. Deep Learning takes inspiration from human cognitive processes and is similar to human intuition. As a result, Go programs developed with Deep Learning generate plays that feel more human. We use Deep Learning and Reinforcement learning to develop scaffolding learning system for Go. The system contains human-like Go programs with various strengths, which allows novice players to learn the game progressively. We also introduced a simplified variant of Go, named Jungo. The game could help the beginners to learning the game of Go.
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Acknowledgment
The authors would like to thank anonymous referees for their valuable comments in improving the overall quality of this paper. This work was supported in part by the Ministry of Science and Technology of Taiwan under the contract 106-2511-S-259-001-MY3 and the contract 108-2634-F-259-001- through Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan.
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Yen, SJ., Chen, YL., Lin, HI. (2019). Scaffolding Learning for the Novice Players of Go. In: Rønningsbakk, L., Wu, TT., Sandnes, F., Huang, YM. (eds) Innovative Technologies and Learning. ICITL 2019. Lecture Notes in Computer Science(), vol 11937. Springer, Cham. https://doi.org/10.1007/978-3-030-35343-8_15
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DOI: https://doi.org/10.1007/978-3-030-35343-8_15
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