Research on Children’s Game Activity Design Strategies Based on Embodied Cognition Theory

Edutainment,in the kindergarten education stage,emphasizes the game as the basic activity and combines the content of education with the form of the game,thus it also forms the educational method of gamification teaching.Through investigation and analysis,it is found that the current kindergarten game activity design has the problem of improper combination of educational content and game form.The current kindergarten game activity design has problems such as stereotypes,children’s lack of active learning opportunities in activities,teachers’insufficient theoretical understanding,inappropriate teacher guidance methods,and so on.Embodied cognition theory attaches importance to the important role of the body in the development of cognition,provides new guidance for classroom teaching,and opens up a new path for classroom teaching reform.Based on the perspective of embodied cognition theory,the concept of body and mind integration should be adhered to in kindergarten teaching with games as the basic activity,experiential teaching situation should be created,children’s subjective experience should be respected,and games and interactions should be designed to promote children’s physical and mental participation,thus laying a foundation for the realization of children’s individual freedom,autonomy,and all-round development.Therefore,this paper aims at the existing problems in the current kindergarten gamification teaching and discusses the design strategy of children’s game activities based on embodied cognition theory.

Improving Behavior of Computer Game Bots Using Fictitious Play

In modern computer games, ＂bots＂ - intelligent realistic agents play a prominent role in the popularity of a game in the market. Typically, bots are modeled using finite-state machine and then programmed via simple conditional statements which are hard-coded in bots logic. Since these bots have become quite predictable to an experienced games＇ player, a player might lose interest in the game. We propose the use of a game theoretic based learning rule called fictitious play for improving behavior of these computer game bots which will make them less predictable and hence, more a enjoyable game.

Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player analytics.Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game.Player behavior focuses on dynamic and static information gathered at the time of gameplay.Player experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings.In this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral devices.Long Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer learning.Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems.The homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’engagement.Relevant not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.

A Playing Game——On the Relationship between Fiction & Reality in Continuityof Parks

In postmodernist novel, the explanation of the relationship between fiction and reality is a distinctive feature, known as a playing game. In Continuity of Parks, the "game" is played through the application of colors, settings, props, etc. with the plots' evolution. It is helpful to appreciate the postmodernist novel by analysing the relationship.

PLAY、GAME、SPORT三者关系学说对当前我国学校体育教学改革的启示

文章采用文献资料法、逻辑分析法等,对PLAY、GAME、SPORT三者关系学说以及当前我国学校体育教学存在的问题进行梳理与分析,在此基础上提出当前我国学校体育教学改革的策略。认为PLAY、GAME、SPORT三者关系学说给我国当前学校体育教学改革的启示在于将Game,特别是Play融进我们当前学校体育教学的指导思想里;使教学内容由原来竞技化体系向生活化、娱乐化、实用化、方法化、时代化和"玩耍化"相结合的方向转变;不能重Sport而不重play和game,重本本而不重多样,要形成一种有我国特色的学校体育教学模式,要以"育人为本,健康第一"的学校体育教学理念为指导,寓教于趣、寓教于乐,使学校体育教学能切合学生的实际需要。

Path Planning of the Multiple Mobile Robot System Applied in Chinese Chess Game

The article presents the path planning algorithm to be applied in the Chinese chess game, and uses multiple mobile robots to present the experimental scenario. Users play the Chinese chess game using the mouse on the supervised computer. The supervised computer programs the motion paths using A＊ searching algorithm, and controls mobile robots moving on the grid based chessboard platform via wireless radio frequency （RF） interface. The A＊ searching algorithm solves shortest path problems of mobile robots from the start point to the target point, and avoids the obstacles on the chessboard platform. The supervised computer calculates the total time to play the game, and computes the residual time to play chess in the step for each player. The simulation results can fired out the shortest motion paths of the mobile robots （chesses） moving to target points from start points in the monitor, and decides the motion path to be existence or not. The eaten chess can moves to the assigned position, and uses the A＊ searching algorithm to program the motion path, too. Finally, the authors implement the simulation results on the chessboard platform using mobile robots. Users can play the Chinese chess game on the supervised computer according to the Chinese chess game rule, and play each step of the game in the assigned time. The supervised computer can suggests which player don＇t obey the rules of the game, and decides which player to be a winner. The scenario of the Chinese chess game feedback to the user interface using the image system.

A New Action-Based Reasoning Approach for Playing Chess

Many previous research studies have demonstrated game strategies enabling virtual players to play and take actions mimicking humans.The CaseBased Reasoning(CBR)strategy tries to simulate human thinking regarding solving problems based on constructed knowledge.This paper suggests a new Action-Based Reasoning(ABR)strategy for a chess engine.This strategy mimics human experts’approaches when playing chess,with the help of the CBR phases.This proposed engine consists of the following processes.Firstly,an action library compiled by parsing many grandmasters’cases with their actions from different games is built.Secondly,this library reduces the search space by using two filtration steps based on the defined action-based and encoding-based similarity schemes.Thirdly,the minimax search tree is fed with a list extracted from the filtering stage using the alpha-beta algorithm to prune the search.The proposed evaluation function estimates the retrievably reactive moves.Finally,the best move will be selected,played on the board,and stored in the action library for future use.Many experiments were conducted to evaluate the performance of the proposed engine.Moreover,the engine played 200 games against Rybka 2.3.2a scoring 2500,2300,2100,and 1900 rating points.Moreover,they used the Bayeselo tool to estimate these rating points of the engine.The results illustrated that the proposed approach achieved high rating points,reaching as high as 2483 points.

Approximating Ordinary Differential Equations by Means of the Chess Game Moves

The chess game provides a very rich experience in neighborhood types. The chess pieces have vertical, horizontal, diagonal, up/down or combined movements on one or many squares of the chess. These movements can associate with neighborhoods. Our work aims to set a behavioral approximation between calculations carried out by means of traditional computation tools such as ordinary differential equations (ODEs) and the evolution of the value of the cells caused by the chess game moves. Our proposal is based on a grid. The cells’ value changes as time pass depending on both their neighborhood and an update rule. This framework succeeds in applying real data matching in the cases of the ODEs used in compartmental models of disease expansion, such as the well-known Susceptible-Infected Recovered (SIR) model and its derivatives, as well as in the case of population dynamics in competition for resources, depicted by the Lotke-Volterra model.

Playing the Traditional Game

FEW games are as popular in China as mahjong. whose players range in age from 20 to over 80. Today mahjong, a favorite pastime for Chinese natives, is also winning hearts of the expat community in China.

Playing a Dangerous Game

China＇s agriculture has grown rapidly over the last 50 years, bringing selfsufficiency and economic development to the world＇s most populous country. However, Jiang Gaoming, a researcher with the Institute of Botany at the Chinese Academy of Sciences,

改进深度神经网络在爱恩斯坦棋中的应用研究

爱恩斯坦棋作为一种附带随机性的完美信息博弈,其难度在于每次投掷骰子导致的结果不确定性,这增加了策略设计和局面的评估难度。针对爱恩斯坦棋的游戏规则,提出了一种改进的深度学习方法。对Alpha(go)Zero神经网络模型进行改进和设计,使其能精确地评估各种棋盘状态,生成有效的游戏策略。通过结合改进的残差神经网络和蒙特卡洛树搜索,提取棋局特征并进行局面评估,动态生成策略和进行决策。结合强化学习,以期望胜率为准则,通过自我对弈不断优化权重,改进策略生成效果。实验结果表明:改进的深度学习方法优于全国计算机博弈大赛冠军组算法,进一步验证了深度学习方法在爱恩斯坦棋随机性完美信息博弈中的有效性和可行性。

长江经济带生态文明建设“一盘棋”重要论述中的系统观念

文章旨在研究阐释习近平生态文明思想的科学内涵,从新时代“五位一体”总体布局、区域协调发展战略和中华优秀传统生态文化成果滋养润育的宏观视野入手论证,深入解读习近平总书记关于长江经济带生态文明建设“一盘棋”重要论述中系统观念所蕴含的战略思维、底线思维、创新思维、系统思维,进一步深刻领会习近平生态文明思想的理论特质、原创性贡献、实践伟力。

融合经验知识与深度强化学习的久棋Alpha-Beta算法优化研究

藏族久棋作为一种传统的棋类博弈游戏,具备高度复杂的规则体系以及变幻莫测的棋局演变。传统的博弈策略在面对不同对手和棋局时不稳定,性能差,需要新的方法提高藏族久棋AI的博弈水平。以藏族久棋为研究对象,针对布局阶段,改进传统Alpha-Beta剪枝搜索算法,并结合经验知识,融入深度强化学习算法完成棋盘布局合理性的落子选择,以此为后续阶段铺路。在行棋阶段与飞子阶段,结合经验知识使用Alpha-Beta算法,完成行棋路径。最后,将所提算法和策略集成于久棋AI程序,在中国计算机博弈锦标赛中取得了良好的成绩,验证了该方法的有效性。

基于Alpha-beta剪枝树的揭棋算法的设计与实现

揭棋是中国象棋的一个变种玩法,相较于中国象棋策略、收益皆透明的模式,揭棋无法确定收益和后续策略,属于非完全信息博弈,需要开发新的算法才能实现揭棋人机对弈。文章设计并实现了基于Alpha-beta剪枝技术辅以启发式搜索的揭棋程序,通过创造极大层与极小层之间的暗子扩张层构建出适合揭棋使用的博弈树结构,基于中国象棋的分值评价标准设计了适用于揭棋的评分体系,解决了对暗子的评分与深层搜索问题,实现了对揭棋状态复杂度与揭棋算法的初步探索。

幼小衔接视角下自主棋游戏助推幼儿思维发展的实践研究

当前幼儿园里的棋游戏占比较高的是传统棋游戏及游戏类棋游戏,前者对孩子识记和运用既定游戏规则的要求比较高,故而于孩子而言难度相对更大。自主棋游戏是幼儿在已有行棋经验、生活经验的基础上,自主设计玩法、规则,进行合作行棋的游戏。6-8岁幼儿正处于抽象思维的萌芽期,大班阶段自主棋游戏具有推动与促进幼儿思维与心智发展的优势,相较传统棋游戏更能促进幼儿的成长。从幼小衔接视角出发,大班幼儿自主棋游戏助推幼儿思维发展的实施策略包含“以趣味激发兴趣、在观察基础上给予指导、以实战增加经验”等,以促进幼儿智力和非智力因素的发展,更优质地提升幼小衔接。

A Matrix Approach to the Modeling and Analysis of Networked Evolutionary Games With Time Delays

Using the semi-tensor product method, this paper investigates the modeling and analysis of networked evolutionary games(NEGs) with finite memories, and presents a number of new results. Firstly, a kind of algebraic expression is formulated for the networked evolutionary games with finite memories, based on which the behavior of the corresponding evolutionary game is analyzed. Secondly, under a proper assumption, the existence of Nash equilibrium of the given networked evolutionary games is proved and a free-type strategy sequence is designed for the convergence to the Nash equilibrium. Finally, an illustrative example is worked out to support the obtained new results.

An Analysis of the Connection Between AI Development and the Creation of Games and Advanced Computer Systems

Electronic games and video games have engaged the interest of artificial intelligence(AI)researchers.The cryptocurrency bitcoin is generated by an algorithm based on cryptography technology.High frequency trading(HFT)based on high quality software.The most spectacular games and programs use a learning neural network,for example backgammon,bridge,Go.Those were created with the use of“deep learning”on multiple layers of neural network.Sometimes,it is not clear“which path the computer will chose”.The paper aims at discussing the electronic version of some games and their applications in developing an AI.

On Positional Games With Perfect Information and Their Applications

The game theory was firstly used for description of economic phenomena and social interaction. But there are certain type of perfect information games （PI-games）, the so-called positional game or Banach-Mazur games, which so far have not been applied in economy. The perfect information positional game is defined as the game during which at any time the choice is made by one of the players who is acquainted with the previous decision of his opponent. The game is run on a sequential basis. The aim of this paper is to discuss selected Banach-Mazur games and to present some applications of positional game. This paper also shows new theoretical example of a determined PI-game, based by theoretical overview. All considerations are pure theoretical and based by logical deduction.