The effect of action video game experience on task-switching

There is now a substantial body of work demonstrating that action video game experience results in enhancements in a wide variety of perceptual skills. More recently, several groups have also demonstrated improvements in abilities that are more cognitive in nature, in particular, the ability to efficiently switch between tasks. In a series of four experiments, we add to this body of work, demonstrating that the action video game player advantage is not exclusively due to an ability to map manual responses onto arbitrary buttons, but rather generalizes to vocal responses, is not restricted to tasks that are perceptual in nature (e.g. respond to a physical dimension of the stimulus such as its color), but generalizes to more cognitive tasks (e.g. is a number odd or even), and is present whether the switch requires a goal-switch or only a motor switch. Finally, a training study establishes that the relationship between the reduction in switch cost and action game playing is causal.     

Self-emergence of a common lexicon by evolution in teams of autonomous agents

In multi-agent systems, the study of language and communication is an active field of research. In this paper we present the application of evolutionary strategies to the self-emergence of a common lexicon in a population of agents. By modeling the vocabulary or lexicon of each agent as an association matrix or look-up table that maps the meanings (i.e. the objects encountered by the agents or the states of the environment itself) into symbols or signals we check whether it is possible for the population to converge in an autonomous, decentralized way to a common lexicon, so that the communication efficiency of the entire population is optimal. We have conducted several experiments aimed at testing whether it is possible to converge with evolutionary strategies to an optimal Saussurean communication system. We have organized our experiments alongside two main lines: first, we have investigated the effect of the population size on the convergence results. Second, and foremost, we have also investigated the effect of the lexicon size on the convergence results. To analyze the convergence of the population of agents we have defined the population's consensus when all the agents (i.e. 100% of the population) share the same association matrix or lexicon. As a general conclusion we have shown that evolutionary strategies are powerful enough optimizers to guarantee the convergence to lexicon consensus in a population of autonomous agents.     

Finite-horizon neuro-optimal tracking control for a class of discrete-time nonlinear systems using adaptive dynamic programming approach

In this paper, a finite-horizon neuro-optimal tracking control strategy for a class of discrete-time nonlinear systems is proposed. Through system transformation, the optimal tracking problem is converted into designing a finite-horizon optimal regulator for the tracking error dynamics. Then, with convergence analysis in terms of cost function and control law, the iterative adaptive dynamic programming (ADP) algorithm via heuristic dynamic programming (HDP) technique is introduced to obtain the finite-horizon optimal tracking controller which makes the cost function close to its optimal value within an ?-error bound. Three neural networks are used as parametric structures to implement the algorithm, which aims at approximating the cost function, the control law, and the error dynamics, respectively. Two simulation examples are included to complement the theoretical discussions.     

Evidence-based guidelines for the informal use of computers by children to promote the development of academic,cognitive and social skills

The use of computers in the home has become very common among young children. This paper reviews research on the effects of informal computer use and identifies potential pathways through which computers may impact children's development. Based on the evidence reviewed, we present the following guidelines to arrange informal computer experiences that will promote the development of children's academic, cognitive and social skills: (1) children should be encouraged to use computers for moderate amounts of time (2–3 days a week for an hour or two per day) and (2) children's use of computers should (a) include non-violent action-based computer games as well as educational games, (b) not displace social activities but should instead be arranged to provide opportunities for social engagement with peers and family members and (c) involve content with pro-social and non-violent themes. We conclude the paper with questions that must be addressed in future research.

Practitioner Summary: This paper reviews research on the effects of informal computer use on children's academic, cognitive and social skills. Based on the evidence presented, we have presented guidelines to enable parents, teachers and other adults to arrange informal computer experiences so as to maximise their potential benefit for children's development.     

学校球类与瑞士赛制相关研究

推动瑞士赛制移植研究,使瑞士赛制独立于项目之外,可以在球类项目中运用,解决球类传统赛制存在的问题,改革学校球类赛制,创造更多学生参与体育锻炼的机会,充分调动学生参与球类竞赛的积极性。     

北京科技大学体育馆赛后利用问题的探讨

通过对北京科技大学体育馆的案例分析,对体育建筑的多功能设计和赛后利用转型进行探究.运用国内外的相关理论,总结出赛后利用的几种对策.对北京科技大学体育馆实地考察和调研,思考其使用现状与建筑设计的关系.     

Research of Multiagent Coordination and Cooperation Algorithm

To solve the problem of conflict and deadlock with agents in multiagent system, an algorithm of multiagent coordination and cooperation was proposed. Taking agent in multiagent system as a player, the pursuit problem Markov model was built. The solution was introduced to get the optimal Nash equilibrium by multiagent reinforcement learning. The method of probability and statistics and Bayes formula was used to estimate the policy knowledge of other players. Relative mean deviation method was used to evaluate the confidence degree in order to increase the convergence speed. The simulation results on pursuit problem showed the feasibility and validity of the given algorithm.     

可数状态空间零和随机对策期望折扣赔付准则

讨论离散时间可数状态空间下零和随机对策期望折扣赔付准则模型。给出了一组保证期望折扣最优平稳策略存在的最优性条件。在这些条件下证明了期望折扣最优方程解和最优平稳策略的存在性。     

基于SDL＆OpenGL的跨平台三维游戏中的关键技术研究

三维游戏正处在蓬勃发展的时期,跨平台三维游戏开发技术中涉及的优化技术仍然是值得关注和难于掌握的.文章从跨平台技术开始,以一款具体的三维游戏设计为例,分析了三维游戏中的各种功能实现和优化技术,包括纹理技术、列表技术、高级字体技术、IBR技术、混合技术以及绘制顺序的优化策略等,并通过运用这些优化技术来得到较好的游戏结果.     

基于神经网络的微分对策控制器设计

采用伴随－BP技术，将微分对策的两点边值求解问题转化两个神经网络的学习问题，训练后的两个神经网络分别作为对策双方的最优控制器在线使用，避免了直接求解复杂的两点边值问题，对追逃微分对策问题的仿真结果表明，该方法对初始条件和噪声具有较好的鲁棒性。