### 数学代考|计算复杂性理论代写computational complexity theory代考|Agent Based Modeling and Artificial Life

statistics-lab™ 为您的留学生涯保驾护航 在代写计算复杂性理论computational complexity theory方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写计算复杂性理论computational complexity theory代写方面经验极为丰富，各种代写计算复杂性理论相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 数学代考|计算复杂性理论代写computational complexity theory代考|Definition of the Subject

Agent-based modeling began as the computational arm of artificial life some 20 years ago. Artificial life is concerned with the emergence of order in nature. How do systems self-organize themselves and spontaneously achieve a higher-ordered state? Agent-based modeling then, is concerned with exploring and understanding the processes that lead to the emergence of order through computational means. The essential features of artificial life models are translated into computational algorithms through agent-based modeling. With its historical roots in artificial life, agent-based modeling has become a distinctive form of modeling and simulation. Agent-based modeling is a bottom-up approach to modeling complex systems by explicitly representing the behaviors of large numbers of agents and the processes by which they interact. These essential features are all that is needed to produce at least rudimentary forms of emergent behavior at the systems level. To understand the current state of agent-based modeling and where the field aspires to be in the future, it is necessary to understand the origins of agent-based modeling in artificial life.

## 数学代考|计算复杂性理论代写computational complexity theory代考|Introduction

The field of Artificial Life, or ${ }^{\alpha}$ ALife, $”$ is intimately connected to Agent-Based Modeling, or “ABM.” Although one can easily enumerate some of life’s distinctive properties, such as reproduction, respiration, adaptation, emergence, etc., a precise definition of life remains elusive.

Artificial Life had its inception as a coherent and sustainable field of investigation at a workshop in the late 1980 s [43]. This workshop drew together specialists from diverse fields who had been working on related problems in different guises, using different vocabularies suited to their fields.

At about the same time, the introduction of the personal computer suddenly made computing accessible, convenient, inexpensive and compelling as an experimental tool. The future seemed to have almost unlimited possibilities for the development of ALife computer programs to explore life and its possibilities. Thus several ALife software programs emerged that sought to encapsulate the essential elements of life through incorporation of ALife-related algorithms into easily usable software packages that could be widely distributed. Computational programs for modeling populations of digital organisms, such as Tierra, Avida, and Echo, were developed along with more general purpose agent-based simulators such as Swarm.

Yet, the purpose of ALife was never restricted to understanding or re-creating life as it exists today. According to Langton:
Artificial systems which exhibit lifelike behaviors are worthy of investigation on their own right, whether or not we think that the processes that they mimic have played a role in the development or mechanics of life as we know it to be. Such systems can help us expand our understanding of life as it could be. (p. xvi in [43])

The field of ALife addresses life-like properties of systems at an abstract level by focusing on the information content of such systems independent of the medium in which they exist, whether it be biological, chemical, physical or in silico. This means that computation, modeling, and simulation play a central role in ALife investigations.

The relationship between ALife and ABM is complex. A case can be made that the emergence of ALife as a field was essential to the creation of agent-based modeling. Computational tools were both required and became possible in the 1980 s for developing sophisticated models of digital organisms and general purpose artificial life simulators. Likewise, a case can be made that the possibility for creating agent-based models was essential to making ALife a promising and productive endeavor. ABM made it possible to understand the logical outcomes and implications of ALife models and life-like processes. Traditional analytical means, although valuable in establishing baseline information, were limited in their capabilities to include essential features of ALife. Many threads of ALife are still intertwined with developments in $\mathrm{ABM}$ and vice verse. Agent-based models demonstrate the emergence of lifelike features using ALife frameworks; ALife algorithms are widely used in agent-based models to represent agent behaviors. These threads are explored in this article. In ALife terminology, one could say that ALife and ABM have $C O-$ evolved to their present states. In all likelihood they will continue to do so.

## 数学代考|计算复杂性理论代写computational complexity theory代考|Self-Replication and Cellular Automata

Self-Replication and Cellular Automata Artificial Life traces its beginnings to the work of John von Neumann in the 1940 s and investigations into the theoretical possibilities for developing a self-replicating machine [64]. Such a self-replicating machine not only carries instructions for its operations, but also for its replication. The issue concerned how to replicate such a machine that contained the instructions for its operation along with the instructions for its replication. Did a machine to replicate such a machine need to contain both the instructions for the machine’s operation and replication, as well as instructions for replicating the instructions on how to replicate the original machine? (see Fig. 1). Von Neumann used the abstract mathematical construct of cellular automata, originally conceived in discussions with Stanislaw Ulam, to prove that such a machine could be designed, at least in theory. Von Neumann was never able to build such a machine due to the lack of sophisticated computers that existed at the time.

Cellular automata (CA) have been central to the development of computing Artificial Life models. Virtually all of the early agent-based models that required agents to be spatially located were in the form of von Neumann’s original cellular automata. A cellular automata is a finitestate machine in which time and space are treated as discrete rather than continuous, as would be the case, for example in differential equation models. A typical CA is a two-dimensional grid or lattice consisting of cells. Each cell assumes one of a finite number of states at any time. A cell’s neighborhood is the set of cells surrounding a cell, typically, a five-cell neighborhood (von Neumann neighborhood) or a nine-cell neighborhood (Moore neighborhood), as in Fig. $2 .$

A set of simple state transition rules determines the value of each cell based on the cell’s state and the states of neighboring cells. Every cell is updated at each time according to the transition rules. Each cell is identical in terms of its update rules. Cells differ only in their initial states. A CA is deterministic in the sense that the same state for a cell and its set of neighbors always results in the same updated state for the cell. Typically, CAs are set up with periodic boundary conditions, meaning that the set of cells on one edge of the grid boundary are the neighbor cells to the cells on the opposite edge of the grid boundary. The space of the CA grid forms a surface on a toroid, or donut-shape, so there is no boundary per se. It is straightforward to extend the notion of cellular automata to two, three, or more dimensions.

## 数学代考|计算复杂性理论代写computational complexity theory代考|Introduction

ALife 领域通过关注此类系统的信息内容，在抽象层面解决系统的类生命属性，而这些信息内容独立于它们存在的介质，无论是生物、化学、物理还是计算机。这意味着计算、建模和模拟在 ALife 调查中发挥着核心作用。

ALife 和 ABM 之间的关系很复杂。可以证明，ALife 作为一个领域的出现对于创建基于代理的建模至关重要。在 1980 年代，计算工具既是必需的，也是可能的，用于开发复杂的数字生物模型和通用人工生命模拟器。同样，可以证明创建基于代理的模型的可能性对于使 ALife 成为有前途和富有成效的努力至关重要。ABM 使理解 ALife 模型和栩栩如生的过程的逻辑结果和含义成为可能。传统的分析手段，虽然在建立基线信息方面很有价值，但在包含 ALife 基本特征的能力方面受到限制。ALife 的许多主线仍然与一种乙米反之亦然。基于代理的模型使用 ALife 框架展示了栩栩如生的特征的出现；ALife 算法广泛用于基于代理的模型中来表示代理行为。本文探讨了这些线程。在 ALife 术语中，可以说 ALife 和 ABM 具有C这−进化到现在的状态。他们很可能会继续这样做。

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。