Python论坛的帖子：

Mon Sep 13 11:32:42 HKT 2004

对那个机器人有点兴趣，不过不知道有没有python版的
----- Original Message ----- 
From: "Zoom.Quiet" <zoomq at infopro.cn>
To: "pl w" <python-chinese at lists.python.cn>
Sent: Monday, September 13, 2004 11:07 AM
Subject: Re[2]: [python-chinese] 一个关于智能模拟的Python程序


> Hollo pl:
> 
>   咦咦咦？
> http://www.robochina.org/document.php?cid=8
> 等等！有非常高水平的综合文章 哪？！？！？
> 
> 简单就是美！
> 如果非要机器人理解各种房间的情况，那未是没有底儿的工作了！
> 
> 想象一下子，人来打扫房间是如何的？！？！？
> 
> 第一次，去一个未知的大楼打扫，如何的？！？！？
> 
> 嘿嘿嘿！让机器人有学习能力，而不是预先分析，更加简单而且好用！
> 
> 
> /******** [2004-09-13]11:00:30 ; pl wrote:
> 
> pl w> 是否可以这样，你用一个列表A把要走的这些格给标个号，每走一步从A中pop出这个格的标号。如果A列表中没有数字时，那就是整个都扫完了。然后再选取个最短路径到另一个ROOM
>  
>     
> 
> pl w> Wang Chao <cnw at vip.sina.com> wrote:
> pl w> 老大，那个地方好像人气不足，我也没找到tank设计的相关东西。
> pl w> 但我感觉还是有问题的，应该不是自动走遍所有房间这么简单。
> 前进->>是房间->清扫否?->左转或是右转->是墙?->左转或是右转
> pl w> 除了这些，我还有一个约束条件是清扫过没有，先机算能否上下左右这么走，假如不靠墙能走，就开始去自己的记忆库里搜索打扫过没有，假如清扫过，就不再进入。
> 
> >> Wang Chao> 假设房间是
> >> Wang Chao> 00 01 02 
> >> Wang Chao> 10 11 12
> >> Wang Chao> 20 21 22
> 
> 
> pl w> 假如我从01开始走，轨迹为 00 -> 10 -> 20 -> 21 -> 11 -> 12
> pl w> ，走到这个位置，就只能随机判断下一步是走02 还是 22
> pl w> 但不管走到哪里，都将导致另外一个房间不会被打扫，因为根据碰墙约束和清扫状态约束，机器人就锁死到那个位置了。假如我把清扫状态约束去掉，那岂不是天下大乱了？
> 
> 
> pl w> ----- Original Message ----- 
> pl w> From: "Zoom.Quiet" 
> pl w> To: "Wang Chao" 
> 
> pl w> Sent: Sunday, September 12, 2004 1:04 AM
> pl w> Subject: Re: [python-chinese] 一个关于智能模拟的Python程序
> 
> 
> >> Hollo Wang:
> >> 
> >> 哈哈哈！！
> >> 去：
> >> http://www.robochina.org/index.php
> >> 看一下子，就知道你这个极类似于最傻的tank 设计，
> >> 仅仅是要 可以自动走遍所有房间是也乎！！
> >> 
> >> 其实可以蠕虫，又瞎又聋的，仅仅靠摸索吃过幼年期，
> >> 
> >> 本身是个简单的自动吸尘机器人，不用停，只要
> >> 前进->是房间->清扫否?->左转或是右转->是墙?->左转或是右转
> >> 
> >> 就可以了!
> >> 然后,想法子,打印出路线就可以知道最简单逻辑是否可行!
> >> 
> >> 
> >> /******** [2004-9-12]15:57:50 ; Wang wrote:
> >> 
> >> Wang Chao>
> >> 有一个人工智能相关Python小程序，偶明白是要的什么东西，但是在系统如何走路上想不出来好的算法，而且因为刚接触Python，很多语句也不会写。
> >> 
> >> Wang Chao> 希望大家帮忙看一下。 谢谢~~~~~~。详细情况是这样的
> >> 
> >> 
> >> Wang Chao>
> >> 现在已经有environment.py和Agents.py两个文件（代码附最后）
> >> 
> >> Wang Chao>
> >> 他们用来模拟吸尘器系统来执行一个1*2房间的清扫工作，但是目前的代码里没有任何智能，所以他会随机选择他的三个行动之一（左转，右转，洗尘），而不是根据判断当前的情况来选择行动。
> >> 
> >> >>>> import environment
> >> >>>> from Agents import *
> >> >>>> v=environment.VacuumEnvironment()
> >> >>>> r=RandomAgent(agent_actions)
> >> >>>> v.add_agent(r)
> >> 
> >> >>>> v.run(10)
> >> Wang Chao> Initial room config: {(1, 0): 'Dirty', (0, 0): 'Dirty'}
> >> Wang Chao> Percept: ((1, 0), 'Dirty') Action: Left
> >> Wang Chao> Percept: ((0, 0), 'Dirty') Action: Suck
> >> Wang Chao> Percept: ((0, 0), 'Clean') Action: Suck
> >> Wang Chao> Percept: ((0, 0), 'Clean') Action: Right
> >> Wang Chao> Percept: ((1, 0), 'Dirty') Action: Left
> >> Wang Chao> Percept: ((0, 0), 'Clean') Action: Right
> >> Wang Chao> Percept: ((1, 0), 'Dirty') Action: Left
> >> Wang Chao> Percept: ((0, 0), 'Clean') Action: Left
> >> Wang Chao> Percept: ((0, 0), 'Clean') Action: Left
> >> Wang Chao> Percept: ((0, 0), 'Clean') Action: Suck
> >> Wang Chao> Final room config: {(1, 0): 'Dirty', (0, 0): 'Clean'}
> >> 
> >> Wang Chao>
> >> 这就是执行的结果，当然，房间的初始状态也是随机的，系统的动作也是随机的，所以每次执行都不一样。
> >> 
> >> 
> >> Wang Chao>
> >> 现在要想通过扩展原始代码，将房间环境扩展为3*3大小{(0,0),(0,1),(0.2),(1,0),(1,1),(1,2),(2,0),(2,1),(2,2)}，房间初始状态依然是随机，并且吸尘器的初始位置也随机。要求首先保证所有的房间都会被清扫，其次尽量减少系统的行动次数。
> >> Wang Chao>
> >> 因为房间是3*3了，所以自然而然行动就变成了五个“上，下，左，右，洗尘”
> >> 
> >> Wang Chao> 要重写原来的def percept(self, agent):
> >> Wang Chao> 来返回吸尘器当前看到的状态
> >> 
> >> Wang Chao> 重写原来的execute_action(self, agent, action):
> >> Wang Chao> 通过吸尘器的行动，改变对应的环境状态。
> >> 
> >> Wang Chao>
> >> 刚接触Python，这两个东西还不太会写。不知道能否给出参考代码。
> >> 
> >> Wang Chao> 更困惑的是关于如何走路的算法。
> >> Wang Chao> 假设房间是
> >> Wang Chao> 00 01 02 
> >> Wang Chao> 10 11 12
> >> Wang Chao> 20 21 22
> >> Wang Chao>
> >> 我可以规定到了第一个数字是0就不再执行up的动作，第二个数字是2就不再执行right的动作，类似这样的规则来限制系统的动作执行。但是系统不能因为能走就要走，还要有一个限制，如果已经走过了，是不应该再走的。但是假如我走到了21这个位置，该怎么走随随机选择一个么？假如随机后走到了20，然后发现10和21都走过了，那样岂不是就再也出不去了，22这个房间就再也不能被清扫了。
> >> Wang Chao> 这本身就是矛盾的。
> >> Wang Chao> 比较困惑，不知道有什么好的方法来解决这样的问题。
> >> 
> >> Wang Chao>
> >> 我能想到的是根据9种可能的初始房间号，写9个行走线路，来实现清扫，但这样显然就没有任何智能而言，假如房间扩大到10*10，这个代码就没法写了。
> >> 
> >> Wang Chao> 冥思苦想了两天了，始终不得其解，郁闷~~
> >> 
> >> Wang Chao> 附:environment.py
> >> 
> >> Wang Chao> import random
> >> 
> >> Wang Chao> class Environment:
> >> Wang Chao> """Abstract class representing an
> >> Wang Chao> Environment. 'Real' Environment classes
> >> Wang Chao> inherit from this. Your Environment will
> >> Wang Chao> typically need to implement:
> >> Wang Chao> percept: Define the percept that an agent sees.
> >> Wang Chao> execute_action: Define the effects of executing an action.
> >> Wang Chao> Also update the agent.performance slot.
> >> Wang Chao> The environment keeps a list of .objects
> >> Wang Chao> and .agents (which is a subset
> >> Wang Chao> of .objects). Each agent has a .performance slot, initialized to 0.
> >> Wang Chao> Each object has a .location slot, even
> >> Wang Chao> though some environments may not
> >> Wang Chao> need this."""
> >> 
> >> Wang Chao> def __init__(self,):
> >> Wang Chao> self.objects = []
> >> Wang Chao> self.agents = []
> >> 
> >> Wang Chao> object_classes = [] ## List of classes
> >> Wang Chao> that can go into environment
> >> 
> >> Wang Chao> def percept(self, agent):
> >> Wang Chao> "Return the percept that the agent sees at
> >> Wang Chao> this point. Override this."
> >> Wang Chao> abstract()
> >> 
> >> Wang Chao> def execute_action(self, agent, action):
> >> Wang Chao> "Change the world to reflect this action. Override this."
> >> Wang Chao> abstract()
> >> 
> >> Wang Chao> def default_location(self, object):
> >> Wang Chao> "Default location to place a new object with unspecified location."
> >> Wang Chao> return None
> >> 
> >> Wang Chao> def exogenous_change(self):
> >> Wang Chao> "If there is spontaneous change in the world, override this."
> >> Wang Chao> pass
> >> 
> >> Wang Chao> def is_done(self):
> >> Wang Chao> "By default, we're done when we can't find a live agent."
> >> Wang Chao> return False
> >> 
> >> Wang Chao> def step(self):
> >> Wang Chao> """Run the environment for one time step. If the
> >> Wang Chao> actions and exogenous changes are independent, this method will
> >> Wang Chao> do. If there are interactions between them, you'll need to
> >> Wang Chao> override this method."""
> >> Wang Chao> if not self.is_done():
> >> Wang Chao> actions = [agent.program(self.percept(agent))
> >> Wang Chao> for agent in self.agents]
> >> Wang Chao> for (agent, action) in
> >> Wang Chao> zip(self.agents, actions):
> >> Wang Chao> self.execute_action(agent, action)
> >> Wang Chao> self.exogenous_change()
> >> 
> >> Wang Chao> def run(self, steps=1000):
> >> Wang Chao> """Run the Environment for given number of time steps."""
> >> Wang Chao> self.printInitialState()
> >> Wang Chao> for step in range(steps):
> >> Wang Chao> if self.is_done():
> >> Wang Chao> self.printFinalState()
> >> Wang Chao> return
> >> Wang Chao> self.step()
> >> Wang Chao> self.printFinalState()
> >> 
> >> Wang Chao> def printInitialState(self) :
> >> Wang Chao> """ SHow the initial problem state """
> >> Wang Chao> pass
> >> 
> >> Wang Chao> def printFinalState(self) :
> >> Wang Chao> """ SHow the final problem state """
> >> Wang Chao> pass
> >> 
> >> Wang Chao> def add_object(self, object, location=None):
> >> Wang Chao> """Add an object to the environment, setting its location. Also keep
> >> Wang Chao> track of objects that are agents. Shouldn't
> >> Wang Chao> need to override this."""
> >> Wang Chao> object.location = location or
> >> Wang Chao> self.default_location(object)
> >> Wang Chao> self.objects.append(object)
> >> Wang Chao> if isinstance(object, Agent):
> >> Wang Chao> object.performance = 0
> >> Wang Chao> self.agents.append(object)
> >> Wang Chao> return self
> >> 
> >> Wang Chao> def add_agent(self, ag, location=None) :
> >> Wang Chao> """Add an agent to the environment"""
> >> Wang Chao> ag.location = location or self.default_location(ag)
> >> Wang Chao> ag.performance = 0
> >> Wang Chao> self.agents.append(ag)
> >> Wang Chao> return self
> >> 
> >> 
> >> 
> >> Wang Chao> class VacuumEnvironment (Environment) :
> >> Wang Chao> def __init__(self):
> >> Wang Chao> Environment.__init__(self)
> >> Wang Chao> self.status =
> >> Wang Chao> {(0,0):random.choice(['Clean', 'Dirty']),
> >> Wang Chao> 
> >> Wang Chao> (1,0):random.choice(['Clean', 'Dirty'])}
> >> Wang Chao> def percept(self, agent):
> >> Wang Chao> "Returns the agent's location, and the
> >> Wang Chao> location status (Dirty/Clean)."
> >> Wang Chao> return (agent.location,
> >> Wang Chao> self.status[agent.location])
> >> 
> >> Wang Chao> def printInitialState(self) :
> >> Wang Chao> """ Show the initial problem state """
> >> Wang Chao> print "Initial room config: ", self.status
> >> 
> >> Wang Chao> def printFinalState(self) :
> >> Wang Chao> """ Show the final problem state """
> >> Wang Chao> print "Final room config: ", self.status
> >> 
> >> 
> >> 
> >> 
> >> Wang Chao> def execute_action(self, agent, action):
> >> Wang Chao> """Change agent's location and/or
> >> Wang Chao> location's status; track performance.
> >> Wang Chao> Score 10 for each dirt cleaned; -1 for each move."""
> >> Wang Chao> if action == 'Right':
> >> Wang Chao> agent.location = (1,0)
> >> Wang Chao> agent.performance -= 1
> >> Wang Chao> elif action == 'Left':
> >> Wang Chao> agent.location = (0,0)
> >> Wang Chao> agent.performance -= 1
> >> Wang Chao> elif action == 'Suck':
> >> Wang Chao> if self.status[agent.location] == 'Dirty':
> >> Wang Chao> agent.performance += 10
> >> Wang Chao> self.status[agent.location] = 'Clean'
> >> 
> >> Wang Chao> def default_location(self, object):
> >> Wang Chao> "Agents start in either location at random."
> >> Wang Chao> return random.choice([(0,0), (1,0)])
> >> 
> >> 
> >> 
> >> 
> >> 
> >> 
> >> Wang Chao> 附:Agents.py
> >> 
> >> 
> >> 
> >> Wang Chao> import random
> >> 
> >> Wang Chao> ### global variable holding all possible agent actions
> >> Wang Chao> agent_actions = ['Left', 'Right', 'Suck']
> >> 
> >> 
> >> Wang Chao> class Agent :
> >> Wang Chao> """An Agent is a subclass of Object with one required slot,
> >> Wang Chao> .program, which should hold a function that takes one argument, the
> >> Wang Chao> percept, and returns an action. (What
> >> Wang Chao> counts as a percept or action
> >> Wang Chao> will depend on the specific environment in
> >> Wang Chao> which the agent exists.) 
> >> Wang Chao> Note that 'program' is a slot, not a
> >> Wang Chao> method. If it were a method,
> >> Wang Chao> then the program could 'cheat' and look at aspects of the agent.
> >> Wang Chao> It's not supposed to do that: the program can only look at the
> >> Wang Chao> percepts. An agent program that needs a
> >> Wang Chao> model of the world (and of
> >> Wang Chao> the agent itself) will have to build and maintain its own model.
> >> Wang Chao> There is an optional slots, .performance,
> >> Wang Chao> which is a number giving
> >> Wang Chao> the performance measure of the agent in its environment."""
> >> 
> >> Wang Chao> def __init__(self):
> >> Wang Chao> self.alive = True
> >> 
> >> Wang Chao> def program(percept):
> >> Wang Chao> return raw_input('Percept=%s; action? ' % percept)
> >> 
> >> 
> >> Wang Chao> ### In the 2 room environment, there are three
> >> Wang Chao> actions: Left, Right and Suck
> >> 
> >> Wang Chao> class RandomAgent(Agent):
> >> Wang Chao> "An agent that chooses an action at random, ignoring all percepts."
> >> Wang Chao> def __init__(self, actions):
> >> Wang Chao> self.actions = actions
> >> 
> >> Wang Chao> def program(self, percept) :
> >> Wang Chao> action = random.choice(self.actions)
> >> Wang Chao> print "Percept: %s Action: %s" % (percept, action)
> >> Wang Chao> return action
> >> 
> >> 
> >> 
> >> ********************************************/
> >> 
> >> -- 
> >> Free as in Freedom
> >> 
> >> Zoom.Quiet 
> >> 
> >> #=========================================#
> >> ]Time is unimportant, only life important![
> >> #=========================================#
> >> 
> >> sender is the Bat!3.0
> >> 
> >> _______________________________________________
> >> python-chinese list
> >> python-chinese at lists.python.cn
> >> http://python.cn/mailman/listinfo/python-chinese
> >> 
> >> _______________________________________________
> pl w> python-chinese list
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> 
> 
>  
>  
>  
>  
> pl w> perrin
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>  
> pl w> 祝你有美好的每一天~
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> ********************************************/
> 
> -- 
> Free as in Freedom
> 
>  Zoom.Quiet                           
> 
> #=========================================#
> ]Time is unimportant, only life important![
> #=========================================#
> 
> sender is the Bat!3.0
> 


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标题：Re: Re[2]: [python-chinese] 一个关于智能模拟的Python程序