LS-VisionDraughts: improving the performance of an agent for checkers by integrating computational intelligence, reinforcement learning and a powerful search method-Reference-Cited by-同舟云学术

LS-VisionDraughts: improving the performance of an agent for checkers by integrating computational intelligence, reinforcement learning and a powerful search method

Published:2014-04-24 Issue:2 Volume:41 Page:525-550
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Neto Henrique Castro,Julia Rita Maria Silva,Caexeta Gutierrez Soares,Barcelos Ayres Roberto Araujo

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

http://link.springer.com/content/pdf/10.1007/s10489-014-0536-y.pdf

Reference64 articles.

1. Abdoos M, Mozayani N, Bazzan ALC (2014) Hierarchical control of traffic signals using q-learning with tile coding. Appl Intell 40(2):201–213

2. Al-Khateeb B, Kendall G (2012) Effect of look-ahead depth in evolutionary checkers. J Comput Sci Tech 27(5):996–1006

3. Al-Khateeb B, Kendall G (2012) Introducing individual and social learning into evolutionary checkers. IEEE Trans Comput Intell AI Games 258–269

4. Barcelos ARA, Julia RMS, Matias R Jr (2011) D-visiondraughts: a draughts player neural network that learns by reinforcement a high performance environment. Eur Symp Artif Neural Netw Comput Intell Mach Learn

5. Baxter J, Trigdell A, Weaver L (1998) Knightcap: a chess program that learns by combining TD(λ) with game-tree search. In: Proceedings 15th international conference on machine learning. Morgan Kaufmann, San Francisco, pp 29–37

Cited by 11 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. Reinforcement Learning for Data Science;Machine Learning for Data Science Handbook;2023

2. Spatial and temporal features unified self-supervised representation learning networks;Robotics and Autonomous Systems;2022-11

3. ADABA: improving the balancing between runtime and accuracy in a new distributed version of the alpha–beta algorithm;Artificial Intelligence Review;2022-09-26

4. A Survey of Planning and Learning in Games;Applied Sciences;2020-06-30

5. Society of Agents;Natural Language Processing;2020