人工智能计算的区块链框架.docx

Blockchain Framework for Artificial Intelligence Computation

Jie You

1,2,*

1

Dasudian Technologies Ltd., Shenzhen, 518057, China

2

Institute of Computer Engineering, Heidelberg University, Heidelberg, 69117, Germany

*

barco@dasudian.com

computational nodes installed in the blockchain competing to approve the records by just solving a

meaningless puzzle. Here, we pose proof-of-work as a reinforcement-learning problem by modeling the

blockchain growing as a Markov decision process, in which a learning agent makes an optimal decision

over the environment’s state, whereas a new block is added and verified. Specifically, we design the

block verification and consensus mechanism as a deep reinforcement-learning iteration process. As a

result, our method utilizes the determination of state transition and the randomness of action selection of a

Markov decision process, as well as the computational complexity of a deep neural network, collectively

to make the blocks not easy to recompute and to preserve the order of transactions, while the blockchain

nodes are exploited to train the same deep neural network with different data samples (state-action pairs)

in parallel, allowing the model to experience multiple episodes across computing nodes but at one time.

business processes

2,3,4

, have been used for industrial advance

5-11

, and have even triggered innovations in

biotech and medical applications

12-16

Blockchain seeks to minimize the role of trust in achieving consensus

mechanisms exit

computational process, such as finding hashes with specific patterns. This consensus algorithm

disincentivizes misbehavior by making it costly for any agent to alter the state, so there is no need for