To overcome the high cost, high risk and poor efficiency of traditional centralized electric energy trading method, this paper proposes an efficient trading mechanism for energy power supply and demand network (EPSDN) based on blockchain smart contract, considering the opening of the sales side market in China. Specifically, the encourage-real-quotation (ERQ) rule was adopted to determine the clearing queue and price, thus smoothing the supply and demand interaction between the EPSDN node. Meanwhile, the blockchain smart contract was introduced into the transaction to form a sealed quotation function, which eliminates the centralization and high cost and solves the poor transparency and trust in traditional transaction. In addition, the transaction efficiency was improved through the construction of an efficient power trading system and a secure trading environment. A case study is given in the end of the paper. Case study shows that the blockchain-based smart contract trading system for the EPSDN can achieve desirable security and effectiveness, and effectively solve the problems of the traditional centralized trading method. The research findings lay solid theoretical and decision-making bases for small-scale transactions in the electric energy market.
In some sense, computer game can be used as a test bed of artificial intelligence to develop intelligent algorithms. The paper proposed a kind of intelligent method: a reinforcement learning model based on temporal difference (TD) algorithm. And then the method is used to improve the playing power of the computer game of a special kind of chess. JIU chess, also called Tibetan Go chess, is mainly played in places where Tibetan tribes gather. Its play process is divided two sequential stages: preparation and battle. The layout at preparation is vital for the successive battle, even for the final winning. Studies on Tibetan JIU chess have focused on Bayesian network based pattern extraction and chess shape based strategy, which do not perform well. To address the low chess power of JIU chess from the view of artificial intelligence, we developed a reinforcement learning model based on temporal difference (TD) algorithm for the preparation stage of JIU. First, the search range was limited within a 6 × 6 area at the center of the chessboard, and the TD learning architecture was combined with chess shapes to construct an intelligent environmental feedback system. Second, optimal state transition strategies were obtained by self-play. In addition, the results of the reinforcement learning model were output as SGF files, which act as a pattern library for the battle stage. The experimental results demonstrate that this reinforcement learning model can effectively improve the playing strength of JIU program and outperform the other methods.INDEX TERMS Artificial intelligence, reinforcement learning, temporal difference algorithm, JIU chess.
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