论文标题
人工智能计算的区块链框架
Blockchain Framework for Artificial Intelligence Computation
论文作者
论文摘要
区块链是一个本质上分布的数据库，可记录参与各方之间的所有交易或数字事件。记录中的每笔交易均由系统中参与者的共识批准和验证，该系统需要解决艰难的数学难题，这被称为工作证明。为了使批准的记录不可变，数学难题并不容易解决，因此消耗了大量的计算资源。但是，在区块链中安装许多计算节点以通过解决毫无意义的拼图来批准记录是有能量的。在这里，我们通过将增长的区块链作为马尔可夫决策过程进行建模，在该过程中提出工作证明，其中学习代理在环境状态下做出最佳决策，而添加了新的区块并进行了验证。具体而言，我们将块验证和共识机制设计为深入的加强学习迭代过程。 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节点，但一次。我们的方法用于设计下一代公共区块链网络，该网络不仅有可能为工业应用程序省去计算资源，而且还可以鼓励数据共享和AI模型设计，以解决常见问题。
Blockchain is an essentially distributed database recording all transactions or digital events among participating parties. Each transaction in the records is approved and verified by consensus of the participants in the system that requires solving a hard mathematical puzzle, which is known as proof-of-work. To make the approved records immutable, the mathematical puzzle is not trivial to solve and therefore consumes substantial computing resources. However, it is energy-wasteful to have many 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. Our method is used to design the next generation of public blockchain networks, which has the potential not only to spare computational resources for industrial applications but also to encourage data sharing and AI model design for common problems.
