When blockchain meets AI: Optimal mining strategy achieved by machine learning

International Journal of Intelligent SystemsVolume 36, Issue 5 p. 2183-2207 RESEARCH ARTICLE When blockchain meets AI: Optimal mining strategy achieved by machine learning Taotao Wang, Taotao Wang orcid.org/0000-0001-9454-4997 College of Electronics and Information Engineering, Shenzhen University, Shenzhen, ChinaSearch for more papers by this authorSoung Chang Liew, Corresponding Author Soung Chang Liew soung@ie.cuhk.edu.hk orcid.org/0000-0001-7055-6483 Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China Correspondence Soung Chang Liew, Department of Information Engineering, The Chinese University of Hong Kong, Ho Sin Hang Engineering Building, Rm 709, Hong Kong SAR, China. Email: soung@ie.cuhk.edu.hkSearch for more papers by this authorShengli Zhang, Shengli Zhang orcid.org/0000-0002-7937-5870 College of Electronics and Information Engineering, Shenzhen University, Shenzhen, ChinaSearch for more papers by this author Taotao Wang, Taotao Wang orcid.org/0000-0001-9454-4997 College of Electronics and Information Engineering, Shenzhen University, Shenzhen, ChinaSearch for more papers by this authorSoung Chang Liew, Corresponding Author Soung Chang Liew soung@ie.cuhk.edu.hk orcid.org/0000-0001-7055-6483 Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China Correspondence Soung Chang Liew, Department of Information Engineering, The Chinese University of Hong Kong, Ho Sin Hang Engineering Building, Rm 709, Hong Kong SAR, China. Email: soung@ie.cuhk.edu.hkSearch for more papers by this authorShengli Zhang, Shengli Zhang orcid.org/0000-0002-7937-5870 College of Electronics and Information Engineering, Shenzhen University, Shenzhen, ChinaSearch for more papers by this author First published: 01 February 2021 https://doi.org/10.1002/int.22375Citations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract This study applies reinforcement learning (RL) from the AI machine learning field to derive an optimal Bitcoin-like blockchain mining strategy. A salient feature of the RL learning framework is that an optimal (or near-optimal) strategy can be obtained without knowing the details of the blockchain network model. Previously, the most profitable mining strategy was believed to be honest mining encoded in the default blockchain protocol. It was shown later that it is possible to gain more mining rewards by deviating from honest mining. In particular, the mining problem can be formulated as a Markov Decision Process (MDP) which can be solved to give the optimal mining strategy. However, solving the mining MDP requires knowing the values of various parameters that characterize the blockchain network model. In real blockchain networks, these parameter values are not easy to obtain and may change over time. This hinders the use of the MDP model-based solution. In this study, we employ RL to dynamically learn a mining strategy with performance approaching that of the optimal mining strategy. Since the mining MDP problem has a nonlinear objective function (rather than linear functions of standard MDP problems), we design a new multidimensional RL algorithm to solve the problem. Experimental results indicate that, without knowing the parameter values of the mining MDP model, our multidimensional RL mining algorithm can still achieve optimal performance over time-varying blockchain networks. Citing Literature Volume36, Issue5May 2021Pages 2183-2207 RelatedInformation