Agent-CA的体育场馆人群疏散模型

根据体育场馆人群疏散的特点与规律,提出一种基于多智能体和元胞自动机相融合的大型体育场馆人群疏散模型（Agent-CA）。将元胞空间中被虚拟人个体占据的元胞视为一个独立的智能体,将元胞及其状态进行封装,扩展为具有自主性的智能体,通过设计各种人群疏散行为策略做为演化规则,实现个体的差异性以体现个人个性、体力、心理等对疏散行为的影响,对体育场馆的人群疏散进行仿真实验。结果表明,Agent-CA综合了多智能体和元胞自动机的优点,充分考虑了个体内在因素,更接近现实大型体育场馆的人群疏散情形,缩短了疏散时间。     

基于突发事件影响力传播的双向人流疏散仿真

当突发事件发生时,行人行走行为会因为突发事件本身以及突发事件在人群中的传播而改变。对于突发事件影响下的行人行走特征的研究能够提高人流疏散的效率。针对现有研究中数据获取方式的不足,对真实行人场景视频进行图像处理,提取相关数据后分析了无突发事件时行人一般行走特性。针对突发事件下的行人流,利用k-邻近算法和合力的思想描述了突发事件的影响传播和突发事件下行人流的自组织现象,并由此提出一种新的元胞自动机模型,该模型中的行人元胞会受到正常行走、突发事件、安全标识这三个因素所抽象产生的三个作用力的影响。利用模型对突发情况下的双向人流疏散进行仿真,实验结果表明,当安全标识的距离为0、10、20个元胞时,在小范围行人通道中安全标识分布的距离对人群疏散作用不明显;通过对人群间是否存在影响力的研究发现,疏散的效果主要受到附近行人对突发事件传播的影响;突发事件的影响程度太大或影响范围过小都会引发拥堵,不利于人群的疏散。仿真结果与真实世界中的双向行人流疏散情况基本吻合。     

自行车CA模型的元胞长度动态修正

采用固定元胞单元尺寸建立自行车流元胞自动机(CA)模型会产生仿真偏差。为此,提出一种基于现场视频检测数据的元胞尺寸标定方法。通过统计分析车头间距、横向间距与车速的关系,设计一个动态修正元胞长度的函数。对比修正前后模型演化获得的宏观三参数值,可以发现,修正函数能较好地描述跟车距离与车速的动态关系,克服模型因采用定长度元胞单元而造成的面积密度与流率偏差。     

Learning Fallible Deterministic Finite Automata

We consider the problem of learning from a fallible expert that answers all queries about a concept, but often gives incorrect answers. The expert can also be thought of as a truth table describing the concept which has been partially corrupted. In order to learn the underlying concept with arbitrarily high precision, we would like to use its structure in order to correct most of the incorrect answers. We assume that the expert's errors are uniformly and independently distributed, occur with any fixed probability strictly smaller than 1/2, and are persistent. In particular, we present a polynomial time algorithm using membership queries for correcting and learning fallible Deterministic Finite Automata under the uniform distribution.     

The impact of artificial intelligence on leisure

Two views of AI in leisure and the work-place and two views of society are discussed. There is a conceptualisation of AI systems enhancing people in their work and leisure and another of AI automata which tends to degrade and replace human activity. Researchers tend to resolve into Optimists who work within a micro-sociological view and see AI systems as inevitable and beneficent. Others are Pessimists who adopt a macro-sociological view and see AI in its automata role and deliterious social consequences. These polarised perspectives must be integrated as only enhancing AI is socially acceptable.     

Cellular ants: A method to create collision free trajectories for a cooperative robot team

Creating collision-free trajectories for mobile robots, known as the path planning problem, is considered to be one of the basic problems in robotics. In case of multiple robotic systems, the complexity of such systems increases proportionally with the number of robots, due to the fact that all robots must act as one unit to complete one composite task, such as retaining a specific formation. The proposed path planner employs a combination of Cellular Automata (CA) and Ant Colony Optimization (ACO) techniques in order to create collision-free trajectories for every robot of a team while their formation is kept immutable. The method reacts with obstacle distribution changes and therefore can be used in dynamical or unknown environments, without the need of a priori knowledge of the space. The team is divided into subgroups and all the desired pathways are created with the combined use of a CA path planner and an ACO algorithm. In case of lack of pheromones, paths are created using the CA path planner. Compared to other methods, the proposed method can create accurate collision-free paths in real time with low complexity while the implemented system is completely autonomous. A simulation environment was created to test the effectiveness of the applied CA rules and ACO principles. Moreover, the proposed method was implemented in a system using a real world simulation environment, called Webots. The CA and ACO combined algorithm was applied to a team of multiple simulated robots without the interference of a central control. Simulation and experimental results indicate that accurate collision free paths could be created with low complexity, confirming the robustness of the method.