"""Implement Agents and Environments (Chapters 1-2).
The class hierarchies are as follows:
Object ## A physical object that can exist in an environment
Agent
Wumpus
RandomAgent
ReflexVacuumAgent
...
Dirt
Wall
...
Environment ## An environment holds objects, runs simulations
XYEnvironment
VacuumEnvironment
WumpusEnvironment
EnvFrame ## A graphical representation of the Environment
"""
from utils import *
import random, copy, operator
class Object:
"""This represents any physical object that can appear in an Environment.
You subclass Object to get the objects you want. Each object can have a
.__name__ slot (used for output only)."""
def __repr__(self):
return '<%s>' % getattr(self, '__name__', self.__class__.__name__)
def is_alive(self):
"""Objects that are 'alive' should return true."""
return hasattr(self, 'alive') and self.alive
def display(self, canvas, x, y, width, height):
"""Display an image of this Object on the canvas."""
pass
class Agent(Object):
"""An Agent is a subclass of Object with one required slot,
.program, which should hold a function that takes one argument, the
percept, and returns an action. (What counts as a percept or action
will depend on the specific environment in which the agent exists.)
Note that 'program' is a slot, not a method. If it were a method,
then the program could 'cheat' and look at aspects of the agent.
It's not supposed to do that: the program can only look at the
percepts. An agent program that needs a model of the world (and of
the agent itself) will have to build and maintain its own model.
There is an optional slots, .performance, which is a number giving
the performance measure of the agent in its environment."""
def __init__(self):
def program(percept):
return raw_input('Percept=%s; action? ' % percept)
self.program = program
self.alive = True
def TraceAgent(agent):
"""Modify the agent's program to print its input and output. This will let
you see what the agent is doing in the environment."""
old_program = agent.program
def new_program(percept):
action = old_program(percept)
print '%s perceives %s and does %s' % (agent, percept, action)
return action
agent.program = new_program
return agent
class TableDrivenAgent(Agent):
"""This agent selects an action based on the percept sequence.
It is practical only for tiny domains.
To customize it you provide a table to the constructor. [Fig. 2.7]"""
def __init__(self, table):
"Supply as table a dictionary of all {percept_sequence:action} pairs."
## The agent program could in principle be a function, but because
## it needs to store state, we make it a callable instance of a class.
percepts = []
def program(percept):
percepts.append(percept)
action = table.get(tuple(percepts))
return action
self.program = program
class RandomAgent(Agent):
"An agent that chooses an action at random, ignoring all percepts."
def __init__(self, actions):
self.program = lambda percept: random.choice(actions)
loc_A, loc_B = (0, 0), (1, 0) # The two locations for the Vacuum world
class ReflexVacuumAgent(Agent):
"A reflex agent for the two-state vacuum environment. [Fig. 2.8]"
def __init__(self):
def program((location, status)):
if status == 'Dirty': return 'Suck'
elif location == loc_A: return 'Right'
elif location == loc_B: return 'Left'
self.program = program
def RandomVacuumAgent():
"Randomly choose one of the actions from the vaccum environment."
return RandomAgent(['Right', 'Left', 'Suck', 'NoOp'])
def TableDrivenVacuumAgent():
"[Fig. 2.3]"
table = {((loc_A, 'Clean'),): 'Right',
((loc_A, 'Dirty'),): 'Suck',
((loc_B, 'Clean'),): 'Left',
((loc_B, 'Dirty'),): 'Suck',
((loc_A, 'Clean'), (loc_A, 'Clean')): 'Right',
((loc_A, 'Clean'), (loc_A, 'Dirty')): 'Suck',
# ...
((loc_A, 'Clean'), (loc_A, 'Clean'), (loc_A, 'Clean')): 'Right',
((loc_A, 'Clean'), (loc_A, 'Clean'), (loc_A, 'Dirty')): 'Suck',
# ...
}
return TableDrivenAgent(table)
class ModelBasedVacuumAgent(Agent):
"An agent that keeps track of what locations are clean or dirty."
def __init__(self):
model = {loc_A: None, loc_B: None}
def program((location, status)):
"Same as ReflexVacuumAgent, except if everything is clean, do NoOp"
model[location] = status ## Update the model here
if model[loc_A] == model[loc_B] == 'Clean': return 'NoOp'
elif status == 'Dirty': return 'Suck'
elif location == loc_A: return 'Right'
elif location == loc_B: return 'Left'
self.program = program
class Environment:
"""Abstract class representing an Environment. 'Real' Environment classes
inherit from this. Your Environment will typically need to implement:
percept: Define the percept that an agent sees.
execute_action: Define the effects of executing an action.
Also update the agent.performance slot.
The environment keeps a list of .objects and .agents (which is a subset
of .objects). Each agent has a .performance slot, initialized to 0.
Each object has a .location slot, even though some environments may not
need this."""
def __init__(self,):
self.objects = []; self.agents = []
object_classes = [] ## List of classes that can go into environment
def percept(self, agent):
"Return the percept that the agent sees at this point. Override this."
abstract()
def execute_action(self, agent, action):
"Change the world to reflect this action. Override this."
abstract()
def default_location(self, object):
"Default location to place a new object with unspecified location."
return None
def exogenous_change(self):
"If there is spontaneous change in the world, override this."
pass
def is_done(self):
"By default, we're done when we can't find a live agent."
return not find_if(method('is_alive'), self.agents)
def step(self):
"""Run the environment for one time step. If the
actions and exogenous changes are independent, this method will
do. If there are interactions between them, you'll need to
override this method."""
if not self.is_done():
actions = [agent.program(self.percept(agent))
for agent in self.agents]
for (agent, action) in zip(self.agents, actions):
self.execute_action(agent, action)
self.exogenous_change()
def run(self, steps=1000):
"""Run the Environment for given number of time steps."""
for step in range(steps):
if self.is_done(): return
self.step()
def add_object(self, object, location=None):
"""Add an object to the environment, setting its location. Also keep
track of objects that are agents. Shouldn't need to override this."""
object.location = location or self.default_location(object)
self.objects.append(object)
if isinstance(object, Agent):
object.performance = 0
self.agents.append(object)
return self
class XYEnvironment(Environment):
"""This class is for environments on a 2D plane, with locations
labelled by (x, y) points, either discrete or continuous. Agents
perceive objects within a radius. Each agent in the environment
has a .location slot which should be a location such as (0, 1),
and a .holding slot, which should be a list of objects that are
held """
def __init__(self, width=10, height=10):
update(self, objects=[], agents=[], width=width, height=height)
def objects_at(self, location):
"Return all objects exactly at a given location."
return [obj for obj in self.objects if obj.location == location]
def objects_near(self, location, radius):
"Return all objects within radius of location."
radius2 = radius * radius
return [obj for obj in self.objects
if distance2(location, obj.location) <= radius2]
def percept(self, agent):
"By default, agent perceives objects within radius r."
return [self.object_percept(obj, agent)
for obj in self.objects_near(agent)]
def execute_action(self, agent, action):
if action == 'TurnRight':
agent.heading = turn_heading(agent.heading, -1)
elif action == 'TurnLeft':
agent.heading = turn_heading(agent.heading, +1)
elif action == 'Forward':
self.move_to(agent, vector_add(agent.heading, agent.location))
elif action == 'Grab':
objs = [obj for obj in self.objects_at(agent.location)
if obj.is_grabable(agent)]
if objs:
agent.holding.append(objs[0])
elif action == 'Release':
if agent.holding:
agent.holding.pop()
agent.bump = False
def object_percept(self, obj, agent): #??? Should go to object?
"Return the percept for this object."
return obj.__class__.__name__
def default_location(self, object):
return (random.choice(self.width), random.choice(self.height))
def move_to(object, destination):
"Move an object to a new location."
def add_object(self, object, location=(1, 1)):
Environment.add_object(self, object, location)
object.holding = []
object.held = None
self.objects.append(object)
def add_walls(self):
"Put walls around the entire perimeter of the grid."
for x in range(self.width):
self.add_object(Wall(), (x, 0))
self.add_object(Wall(), (x, self.height-1))
for y in range(self.height):
self.add_object(Wall(), (0, y))
self.add_object(Wall(), (self.width-1, y))
def turn_heading(self, heading, inc,
headings=[(1, 0), (0, 1), (-1, 0), (0, -1)]):
"Return the heading to the left (inc=+1) or right (inc=-1) in headings."
return headings[(headings.index(heading) + inc) % len(headings)]
## Vacuum environment
class TrivialVacuumEnvironment(Environment):
"""This environment has two locations, A and B. Each can be Dirty or Clean.
The agent perceives its location and the location's status. This serves as
an example of how to implement a simple Environment."""
def __init__(self):
Environment.__init__(self)
self.status = {loc_A:random.choice(['Clean', 'Dirty']),
loc_B:random.choice(['Clean', 'Dirty'])}
def percept(self, agent):
"Returns the agent's location, and the location status (Dirty/Clean)."
return (agent.location, self.status[agent.location])
def execute_action(self, agent, action):
"""Change agent's location and/or location's status; track performance.
Score 10 for each dirt cleaned; -1 for each move."""
if action == 'Right':
agent.location = loc_B
agent.performance -= 1
elif action == 'Left':
agent.location = loc_A
agent.performance -= 1
elif action == 'Suck':
if self.status[agent.location] == 'Dirty':
agent.performance += 10
self.status[agent.location] = 'Clean'
def default_location(self, object):
"Agents start in either location at random."
return random.choice([loc_A, loc_B])
class Dirt(Object): pass
class Wall(Object): pass
class VacuumEnvironment(XYEnvironment):
"""The environment of [Ex. 2.12]. Agent perceives dirty or clean,
and bump (into obstacle) or not; 2D discrete world of unknown size;
performance measure is 100 for each dirt cleaned, and -1 for
each turn taken."""
def __init__(self, width=10, height=10):
XYEnvironment.__init__(self, width, height)
self.add_walls()
object_classes = [Wall, Dirt, ReflexVacuumAgent, RandomVacuumAgent,
TableDrivenVacuumAgent, ModelBasedVacuumAgent]
def percept(self, agent):
"""The percept is a tuple of ('Dirty' or 'Clean', 'Bump' or 'None').
Unlike the TrivialVacuumEnvironment, location is NOT perceived."""
status = if_(self.find_at(Dirt, agent.location), 'Dirty', 'Clean')
bump = if_(agent.bump, 'Bump', 'None')
return (status, bump)
def execute_action(self, agent, action):
if action == 'Suck':
if self.find_at(Dirt, agent.location):
agent.performance += 100
agent.performance -= 1
XYEnvironment.execute_action(self, agent, action)
## The Wumpus World
class Gold(Object): pass
class Pit(Object): pass
class Arrow(Object): pass
class Wumpus(Agent): pass
class Explorer(Agent): pass
class WumpusEnvironment(XYEnvironment):
object_classes = [Wall, Gold, Pit, Arrow, Wumpus, Explorer]
def __init__(self, width=10, height=10):
XYEnvironment.__init__(self, width, height)
self.add_walls()
## Needs a lot of work ...
def compare_agents(EnvFactory, AgentFactories, n=10, steps=1000):
"""See how well each of several agents do in n instances of an environment.
Pass in a factory (constructor) for environments, and several for agents.
Create n instances of the environment, and run each agent in copies of each one.
Return a list of (agent-name, average-score) tuples."""
envs = [EnvFactory() for i in range(n)]
results = []
for A in AgentFactories:
total = 0
for env in copy.deepcopy(envs):
agent = A()
env.add_object(agent)
env.run(steps)
total += agent.performance
results.append((agent, total / float(n)))
return results
_docex = """
a = ReflexVacuumAgent()
a.program
a.program((loc_A, 'Clean')) ==> 'Right'
a.program((loc_B, 'Clean')) ==> 'Left'
a.program((loc_A, 'Dirty')) ==> 'Suck'
a.program((loc_A, 'Dirty')) ==> 'Suck'
e = TrivialVacuumEnvironment()
e.add_object(TraceAgent(ModelBasedVacuumAgent()))
e.run(5)
agents = [ModelBasedVacuumAgent, ReflexVacuumAgent, TableDrivenVacuumAgent, RandomVacuumAgent]
# For the following set of trials, you should get results of approximately:
# ModelBased: 9, Reflex: 7, TableDriven: 4, Random: 1.7,
compare_agents(TrivialVacuumEnvironment, agents, n=100, steps=4)
"""
# GUI - Graphical User Interface for Environments
# If you do not have Tkinter installed, either get a new installation of Python
# (Tkinter is standard in all new releases), or delete the rest of this file
# and muddle through without a GUI.
import Tkinter as tk
class EnvFrame(tk.Frame):
def __init__(self, env, title='AIMA GUI', cellwidth=50, n=10):
update(self, cellwidth = cellwidth, running=False, delay=1.0)
self.n = n
self.running = 0
self.delay = 1.0
self.env = env
tk.Frame.__init__(self, None, width=(cellwidth+2)*n, height=(cellwidth+2)*n)
#self.title(title)
# Toolbar
toolbar = tk.Frame(self, relief='raised', bd=2)
toolbar.pack(side='top', fill='x')
for txt, cmd in [('Step >', self.env.step), ('Run >>', self.run),
('Stop [ ]', self.stop)]:
tk.Button(toolbar, text=txt, command=cmd).pack(side='left')
tk.Label(toolbar, text='Delay').pack(side='left')
scale = tk.Scale(toolbar, orient='h', from_=0.0, to=10, resolution=0.5,
command=lambda d: setattr(self, 'delay', d))
scale.set(self.delay)
scale.pack(side='left')
# Canvas for drawing on
self.canvas = tk.Canvas(self, width=(cellwidth+1)*n,
height=(cellwidth+1)*n, background="white")
self.canvas.bind('<Button-1>', self.left) ## What should this do?
self.canvas.bind('<Button-2>', self.edit_objects)
self.canvas.bind('<Button-3>', self.add_object)
if cellwidth:
c = self.canvas
for i in range(1, n+1):
c.create_line(0, i*cellwidth, n*cellwidth, i*cellwidth)
c.create_line(i*cellwidth, 0, i*cellwidth, n*cellwidth)
c.pack(expand=1, fill='both')
self.pack()
def background_run(self):
if self.running:
self.env.step()
ms = int(1000 * max(float(self.delay), 0.5))
self.after(ms, self.background_run)
def run(self):
print 'run'
self.running = 1
self.background_run()
def stop(self):
print 'stop'
self.running = 0
def left(self, event):
print 'left at ', event.x/50, event.y/50
def edit_objects(self, event):
"""Choose an object within radius and edit its fields."""
pass
def add_object(self, event):
## This is supposed to pop up a menu of Object classes; you choose the one
## You want to put in this square. Not working yet.
menu = tk.Menu(self, title='Edit (%d, %d)' % (event.x/50, event.y/50))
for (txt, cmd) in [('Wumpus', self.run), ('Pit', self.run)]:
menu.add_command(label=txt, command=cmd)
menu.tk_popup(event.x + self.winfo_rootx(),
event.y + self.winfo_rooty())
#image=PhotoImage(file=r"C:\Documents and Settings\pnorvig\Desktop\wumpus.gif")
#self.images = []
#self.images.append(image)
#c.create_image(200,200,anchor=NW,image=image)
#v = VacuumEnvironment(); w = EnvFrame(v);
#
Copyright:
Peter Norvig, 2002.
#
AIMA:
Python Code,
Example Output.
#
Python.org:
Tutorial,
Language Ref,
Libraries.