Survey of How Human Players Divert In-game Actions for Other Purposes: Towards Human-Like Computer Players-Reference-Cited by-同舟云学术

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Survey of How Human Players Divert In-game Actions for Other Purposes: Towards Human-Like Computer Players

Published:2017 Issue: Volume: Page:243-256
ISSN:0302-9743
Container-title:Entertainment Computing – ICEC 2017
language:
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Author:

Temsiririrkkul Sila,Sato Naoyuki,Nakagawa Kenta,Ikeda Kokolo

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-319-66715-7_27

Reference13 articles.

1. Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529, 484–489 (2016)

2. Mnih, V., et al.: Playing atari with deep reinforcement learning. In: NIPS Deep Learning Workshop 2013 (2013)

3. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529533 (2015)

4. Soni, B., et al.: Bots trained to play like a human are more fun. In: 2008 IEEE International Joint Conference on Neural Networks IJCNN 2008 (IEEE World Congress on Computational Intelligence), pp. 363–369 (2008)

5. Ikeda, K., Viennot, S.: Production of various strategies and position control for Monte-Carlo Go - entertaining human players. In: IEEE Conference on Computational Intelligence and Games (CIG2013), pp. 145–152 (2013)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The Effects of Human-like Modifications to Heuristic Action Evaluation in Video Game Pathfinding;FDG '22: Proceedings of the 17th International Conference on the Foundations of Digital Games;2022-09-05

2. Predicting Human Decision-Making: From Prediction to Action;Synthesis Lectures on Artificial Intelligence and Machine Learning;2018-01-22

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