EINSTein:美国梅隆大学的动态社会网络分析软件指南

2星需积分: 50 104 浏览量更新于2024-08-02 1 收藏 4.27MB PDF 举报

ORA社会网络分析软件是一款由美国梅隆大学开发的专业用于研究社会网络动态分析的工具，特别关注于其在土地作战中的自组织涌现现象探索。该软件名为Enhanced ISAAC Neural Simulation Toolkit (EINSTein)，它构建了一个人工智能实验室环境，通过agent-based模型（基于代理的模型）模拟复杂军事场景，以理解个体行为如何在战场上形成集体智慧和策略。在EINSTein中，核心概念包括： 1. **Agent-Based Models (ABM)**: 这是软件的核心组成部分，通过模拟个体（如士兵、车辆或非生物单位）的行为和互动来描绘战场上的动态网络。每个代理都有自己的决策规则和适应性，这些规则可能根据环境变化而调整。 2. **Universal Grammar of Combat**: 提出了一种通用的战斗法则，探讨了如何通过算法设计使代理在执行任务时展现出类似于自然界中有机体的自组织策略，这有助于研究在未知或变化的战斗环境中如何实现高效协作。 3. **安装与系统需求**：用户指南详细介绍了软件的安装步骤和所需的硬件配置，确保用户的计算机满足运行EINSTein所必需的性能规格。 4. **菜单选项**：软件提供了丰富的菜单选项，如“文件”和“编辑”，允许用户加载、保存和管理各种输入数据，如地形数据、武器参数、遗传算法输入等，以及控制运行过程和参数设置。 5. **具体操作**：例如，“Load EINSTein Input Data File”用于导入初始的模拟数据，“Save Current (1-Sided) Chromosome”用于存储在遗传算法中的进化结果，这些都是用户进行实验和数据分析的关键步骤。 6. **红色数据（Red Data）**: 提到的“Red Data”可能是特定的测试数据或者代表敌方的数据，这在模拟对抗中起着关键作用，帮助研究者评估不同策略和战术的效果。 EINSTein软件的使用不仅限于学术研究，也可用于军事训练和战术决策支持，通过对复杂系统的仿真，为理解和优化社会网络在军事冲突中的动态行为提供了强大的工具。此外，由于其对自我组织和进化机制的模拟，它在其他领域，如城市规划、传染病传播分析或商业策略制定中也可能有应用潜力。

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221

List of figures . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

List of tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231

• On-line genetic algorithm, neural-net, reinforcement-learning,

and pattern recognition toolkits

• On-line data collection and multi-dimensional visualization tools

• On-line chaos-data/time-series analysis tools

• On-line mission-fitness co-evolutionary landscape profilers

EINSTein is the centerpiece of an ambitious on-going project designed

to weave together several inter-related methodological strands. First and

foremost, EINSTein is designed to serve as an interactive "tool box" (or

conceptual laboratory) for the general exploration of combat as a com-

plex adaptive system. As such, analysts have an opportunity to play mul-

tiple "What If?" scenarios and to experiment with fundamental issues of

the dynamics of war.

Secondly, EINSTein provides a collection of on-line data-collection/

data-analysis and pattern recognition tools; this will mark the first time

that many of the tools commonly found in the study of nonlinear dynam-

ical, and complex adaptive, systems will be available in the context of mil-

itary combat analysis.

Thirdly, EINSTein has a data visualization package to facilitate the explo-

ration of multiple high-dimensional co-evolutionary fitness landscapes,

and to foster the development of an intuition of the overall combat

phase space. With embedded genetic algorithm, neural-net, and (in

future versions) reinforcement learning based adaptive learning tool-

kits, EINSTein is an ideal testbed for developing future tactical decision

aids.

Finally, the long-term vision is to extend EINSTein to become a remote

dynamic real-time "combat engine" that assimilates, processes, and auto-

matically analyzes the evolutionary outcomes and consequences of user-

defined scripts, tactics and doctrine. In short, a fully realized intelligent-

agent-based laboratory for exploring collective self-organized emergent behavior in

combat.

Agent-Based Models

Agent-based models of complex adaptive systems are predicated on the

idea that the global behavior of a system derives entirely from the low-

level interactions among its constituent agents. Insights about the real-

world system that the agent-based simulation is designed to model can

then be gained by looking at the emergent structures induced by the

interaction processes taking place within the simulation.

Some recent examples of agent-based models include ECHO (an agent-

based model of natural ecologies [7]), MOAB (the U.S. Geological Sur-

vey’s model of animal behavior [8]), Sugarscape (a model of the evolution

of social systems [9]) and TRANSIM (a model of traffic flow [10]). The

well-known series of SimCity and SimCity--related commercial games (Sim-

Life, SimAnt, etc.) all make heavy use of agent-based methodologies [11].

A more recent example of a commercial game that is also making origi-

nal contributions to adaptive-agent modeling methodology is Cyberlife’s

Creatures (http://www.cyberlife.co.uk/). An extensive web-based listing

of agent-based models appears at URL address

http://hmt.com/

cwr/ibm.html.

Models based on differential equations homogenize the properties of

entire populations and ignore the spatial component altogether. Partial

differential equations--by introducing a physical space to account for

troop movement--fare somewhat better, but still treat the agent popula-

tion as a continuum. In contrast, agent-based-models consist of a discrete

heterogeneous set of spatially distributed individual agents (i.e., combat-

ants), each of which has its own characteristic properties and rules of

behavior. These properties can also change (i.e., adapt) as an individual

agent evolves in time.

In agent-based-models, the final outcome of a battle--as defined, say, by

measuring the surviving force strengths--takes second stage to exploring

how two forces might co-evolve during combat. Such models are

designed to allow the user to explore the evolving patterns of macro-

scopic behavior that result from the collective interactions of individual

agents, as well as the feedback that these patterns might have on the

rules governing the individual agents' behavior.

Vision

The payoff of multiagent-based models of land combat such as EINSTein

is a radically new way of looking at fundamental issues. Such models are

emphatically not to be used for prediction; rather they are best used to

enhance understanding. Specifically, EINSTein is being designed to help

analysts...

• Understand how all of the different elements of combat fit

together in an overall "combat phase space:" Are there regions

that are "sensitive" to small perturbations, and, if so, might there

be a way to exploit this in combat (as in selectively driving an oppo-

nent into more sensitive regions of phase space)?

• Assess the value of information: How can I exploit what I know the

enemy does not know about me?

剩余247页未读，继续阅读

veyochuang

粉丝: 1
资源: 1

EINSTein:美国梅隆大学的动态社会网络分析软件指南

网络分析工具

社会网络分析软件 UCINET 6.708

ora分析脚本

在 ORA 中处理加权、非对称、自循环和断开连接的网络-研究论文

使用Over Time Viewer探索纵向动态网络分析

社会网络分析工具及使用方法

社会网络分析工具，含解析

Pajek蜘蛛：社会网络分析技术（软件）

社会网络分析的经典软件

社会网络分析软件

最新资源