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第573行： − + 第579行：
第579行： − + 第587行：
第587行： − 一般来说，首先需要假定的是，并不是每个人都了解环境，或者哪里有危险，哪里没有危险。根据这个假设，我们可以创建三种类型的代理。第一种类型是训练有素的领导者，这种主体了解环境，能够将知识传播给其他主体，这样他们就知道如何从环境中退出。下一种类型的代理是未经训练的领导者---- 这种代理者不了解环境，然而，当代理者探索环境并从其他类型的领导者那里获得信息时，代理者能够传播有关环境的知识。最后一种类型的代理是跟随者，这种类型的代理只能从其他领导者那里获取信息，不能与其他代理共享信息。+ 第595行：
第595行： − 这些类型代理的实现相当简单。环境中的领导者将环境地图保存为他们的属性之一。一个未经训练的领导者和追随者会以一张空地图作为他们的属性。未经训练的领导者和追随者将开始探索自己的环境，并创建一个适合步行和不适合步行的地点地图。领导者和未受过训练的领导者(一旦他们掌握了知识) ，会根据他们的接近程度与其他代理分享信息。他们将分享关于网格上哪些点被阻塞，本地子图和该地区的危险的信息。+ 第603行：
第603行： − 有两种类型的搜索算法试验了这种实现。有随机搜索和深度优先搜索。随机搜索是指每个主体在环境中朝任何方向前进，并试图找到一条出路。深度优先搜索是指代理程序尽可能沿着一条路径搜索，然后返回，如果它们所经过的路径不包含退出，则尝试另一条路径。如果发现深度优先搜索比随机搜索的速度快15倍。+ − −
→Leader behavior during evacuation simulations： 翻译1
这种模型会对疏散场景有所帮助。以发生火灾的建筑物疏散为例，考虑到个体的特点和他们在团队的表现从而确定人群如何离开建筑的结果，对于设计建筑的布局至关重要。
这种模型会对疏散场景有所帮助。以发生火灾的建筑物疏散为例，考虑到个体的特点和他们在团队的表现从而确定人群如何离开建筑的结果，对于设计建筑的布局至关重要。
=== Leader behavior during evacuation simulations ===
=== Leader behavior during evacuation simulations 疏散模拟中的领导者行为===
As described earlier, the '''Helbing Model''' is used as the basics for crowd behavior. This same type of behavior model is used for evacuation simulations.
As described earlier, the '''Helbing Model''' is used as the basics for crowd behavior. This same type of behavior model is used for evacuation simulations.
As described earlier, the Helbing Model is used as the basics for crowd behavior. This same type of behavior model is used for evacuation simulations.
As described earlier, the Helbing Model is used as the basics for crowd behavior. This same type of behavior model is used for evacuation simulations.
如前所述，Helbing 模型用作群体行为的基础。同样类型的行为模型也被用于疏散模拟。
如前所述，Helbing 模型被用作群体行为的基础。同样类型的行为模型也同样被用于疏散模拟。
In general, the first thing that has to be assumed is that not everyone has knowledge about the environment or where there are and aren't hazards. From this assumption we can create three types of agents. The first type is a trained leader, this agent knows about the environment and is able to spread knowledge to other agents so they know how to exit from an environment. The next type of agent is an untrained leader, this agent does not know about the environment, however, as the agent explores the environment and gets information from other types of leaders, the agent is able to spread the knowledge about the environment. The last type of agent is a follower, this type of agent can only take information from other leaders and not be able to share the information with other agents.
In general, the first thing that has to be assumed is that not everyone has knowledge about the environment or where there are and aren't hazards. From this assumption we can create three types of agents. The first type is a trained leader, this agent knows about the environment and is able to spread knowledge to other agents so they know how to exit from an environment. The next type of agent is an untrained leader, this agent does not know about the environment, however, as the agent explores the environment and gets information from other types of leaders, the agent is able to spread the knowledge about the environment. The last type of agent is a follower, this type of agent can only take information from other leaders and not be able to share the information with other agents.
一般来说，首先需要假定的是，并不是每个人都了解环境或了解哪里有或没有危险。根据这个假设，我们可以创建三种类型的主体。第一种类型是训练有素的领导者，这种主体了解环境并能够将知识传播给其他主体，因此他们知道如何从这种环境中退出。第二种类型的主体是未经训练的领导者。他们不了解环境，但是当这种主体探索环境并从其他类型的领导者那里获得信息时，他们能够传播有关环境的知识。最后一种类型是跟随者，这种类型的主体只能从其他领导者那里获取信息，不能与其他主体共享信息。
The implementation of these types of agents is fairly straightforward. The leaders in the environment have a map of the environment saved as one of their attributes. An untrained leader and followers will start out with an empty map as their attribute. Untrained leaders and followers will start exploring an environment by themselves and create a map of walkable and unwalkable locations. Leaders and untrained leaders (once they have the knowledge), will share information with other agents depending on their proximity. They will share information about which points on the grid are blocked, the local sub-graphs and the dangers in the area.
The implementation of these types of agents is fairly straightforward. The leaders in the environment have a map of the environment saved as one of their attributes. An untrained leader and followers will start out with an empty map as their attribute. Untrained leaders and followers will start exploring an environment by themselves and create a map of walkable and unwalkable locations. Leaders and untrained leaders (once they have the knowledge), will share information with other agents depending on their proximity. They will share information about which points on the grid are blocked, the local sub-graphs and the dangers in the area.
实现这些主体类型的方法相当简单直接。对于领导者，环境地图被保存为他们的属性之一。未经训练的领导者和追随者具有一张空地图的属性。未经训练的领导者和追随者将自己开始探索环境，并创建一个适合和不适合走的地点的地图。领导者和未受过训练的领导者(一旦他们掌握了知识) ，会根据与其他主体的接近程度来分享信息。他们分享的信息包括网格上哪些点被阻塞，局部子地图和该地区的危险之处。
There were two types of searching algorithms tried out for this implementation. There was the random search and the depth first search. A random search is where each of the agents go in any direction through the environment and try to find a pathway out. The depth first search is where agents follow one path as far as it can go then return and try another path if the path they traversed does not contain an exit. If was found that depth first search gave a speed up of 15 times versus a random search.
There were two types of searching algorithms tried out for this implementation. There was the random search and the depth first search. A random search is where each of the agents go in any direction through the environment and try to find a pathway out. The depth first search is where agents follow one path as far as it can go then return and try another path if the path they traversed does not contain an exit. If was found that depth first search gave a speed up of 15 times versus a random search.
有两种类型的搜索算法试验了这种模型：'''随机搜索 Random Search'''和'''深度优先搜索 Depth First Search'''。随机搜索是指每个主体在环境中朝任意方向前进，并试图找到一条出路。深度优先搜索是指主体尽可能沿着一条路径搜索，如果它们所经过的路径不包含出口则返回并尝试另一条路径。结果发现，深度优先搜索比随机搜索的速度快15倍（--[[用户:嘉树|嘉树]]（[[用户讨论:嘉树|讨论]]）If was found 怀疑原文有误，应该是It was found）
=== Scalable simulations ===
=== Scalable simulations ===