陕西战国秦墓出土玻璃和汉紫六博棋子分析研究

利用超景深显微镜（OM）、激光拉曼光谱仪(Raman)、X射线荧光光谱仪(XRF)、扫描电子显微镜能谱(SEM-EDS)、色度仪等多种手段,对陕西战国秦墓出土的两种质地、颜色不同的六博棋子进行了综合分析检测。鉴定出蓝色样品为铅钡玻璃,紫色样品为中国紫制品,发现了中国紫在战国时期的新用途。并对比陕西出土的铅钡玻璃制品与两湖、四川地区出土的同时代的铅钡玻璃制品在成分组成上的差异,推测战国时期铅钡玻璃的制作工艺已成熟,可根据器物形制的需求进行不同的成分配比。同时,还发现当时的玻璃制作已有旧料重熔制作新器的现象。在确定中国紫成分的基础上,利用色度计进行色度测试,根据光谱数据,首次为人造硅酸铜钡颜料确定了特征光谱峰,使快速、无损的对人造硅酸铜钡颜料的鉴定识别成为可能。     

Chess AI: Competing Paradigms for Machine Intelligence

Endgame studies have long served as a tool for testing human creativity and intelligence. We find that they can serve as a tool for testing machine ability as well. Two of the leading chess engines, Stockfish and Leela Chess Zero (LCZero), employ significantly different methods during play. We use Plaskett’s Puzzle, a famous endgame study from the late 1970s, to compare the two engines. Our experiments show that Stockfish outperforms LCZero on the puzzle. We examine the algorithmic differences between the engines and use our observations as a basis for carefully interpreting the test results. Drawing inspiration from how humans solve chess problems, we ask whether machines can possess a form of imagination. On the theoretical side, we describe how Bellman’s equation may be applied to optimize the probability of winning. To conclude, we discuss the implications of our work on artificial intelligence (AI) and artificial general intelligence (AGI), suggesting possible avenues for future research.     

智能型实物棋盘人机对弈象棋机器人的制作简

以物理学原理为基础,结合机械运动原理与电子控制技术。制作了智能型实物棋盘人机对弈象棋机器人。利用霍尔元件感知磁场、电磁铁吸引棋子和落放棋子、三维机械臂移动棋子、单片机程序控制三维机械臂的运动,完成人与机器人对弈象棋的整个过程。结果表明。设计制作的智能型实物棋盘人机对弈象棋机器人完全能与下棋者在实物棋盘下象棋．该机器人下棋能力强,动作自如。     

熵率在中国象棋上的应用

本文通过对加权图上的随机游动熵率的研究,引进了中国象棋各棋子的熵率,从而可以比较中国象棋各棋子的自由度.     

A q-Queens Problem IV. Attacking configurations and their denominators

In Parts I–III we showed that the number of ways to place $q$ nonattacking queens or similar chess pieces on an $n\times n$ chessboard is a quasipolynomial function of $n$ whose coefficients are essentially polynomials in $q$.In this part we focus on the periods of those quasipolynomials. We calculate denominators of vertices of the inside-out polytope, since the period is bounded by, and conjecturally equal to, their least common denominator. We find an exact formula for that denominator of every piece with one move and of two-move pieces having a horizontal move. For pieces with three or more moves, we produce geometrical constructions related to the Fibonacci numbers that show the denominator grows at least exponentially with $q$.     

An optimal minimax algorithm

Computer game-playing programs repeatedly calculate minimax elements = min _i max _j M _ij of large pay off matricesM _ij . A straightforwardrow-by-row calculation of scans rows ofM _ij one at a time, skipping to a new row whenever an element is encountered that exceeds a current minimax. Anoptimal calculation, derived here, scans the matrix more erratically but finds after testing the fewest possible matrix elements. Minimizing the number of elements tested is reasonable when elements must be computed as needed by evaluating future game positions. This paper obtains the expected number of tests required when the elements are independent, identically distributed, random variables. For matrices 50 by 50 or smaller, the expected number of tests required by the row-by-row calculation can be at most 42% greater than the number for the optimal calculation. When the numbersR, C of rows and columns are very large, both calculations require an expected number of tests nearRC/InR.     

On the probability of Magnus Carlsen reaching 2900

How likely is it that Magnus Carlsen will achieve his goal of a 2900 Elo rating? At what level of play does Magnus have a reasonable chance of reaching the 2900 goal? These two questions are of great current interest to Magnus and the chess community. The probabilistic properties of Elo's rating system are well known, and together with a Brownian motion model of rating evolution, we use simulation-based methods to address these questions. Our model assesses that Magnus has a 4.5% chance of reaching 2900 if he continues his 2020–2022 level of play. However, this increases dramatically to $80$% chance if he can repeat his hot streak performance of 2019 which is not an easy undertaking. The probabilities are intimately related to Elo's choice $K$-factor used for grandmaster chess play. Finally, we conclude with a discussion of the policy issues involved with the choice of $K$-factor.