aima-python/agents.py at aima3python2 · lap089/aima-python · GitHub

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"""Implement Agents and Environments (Chapters 1-2).

The class hierarchies are as follows:

Thing ## A physical object that can exist in an environment

Agent

Wumpus

Dirt

Wall

...

Environment ## An environment holds objects, runs simulations

XYEnvironment

VacuumEnvironment

WumpusEnvironment

An agent program is a callable instance, taking percepts and choosing actions

SimpleReflexAgentProgram

...

EnvGUI ## A window with a graphical representation of the Environment

EnvToolbar ## contains buttons for controlling EnvGUI

EnvCanvas ## Canvas to display the environment of an EnvGUI

"""

# TO DO:

# Implement grabbing correctly.

# When an object is grabbed, does it still have a location?

# What if it is released?

# What if the grabbed or the grabber is deleted?

# What if the grabber moves?

# Speed control in GUI does not have any effect -- fix it.

from utils import *

import random, copy

#______________________________________________________________________________

class Thing(object):

"""This represents any physical object that can appear in an Environment.

You subclass Thing to get the things you want. Each thing can have a

.__name__ slot (used for output only)."""

def __repr__(self):

return '<%s>' % getattr(self, '__name__', self.__class__.__name__)

def is_alive(self):

"Things that are 'alive' should return true."

return hasattr(self, 'alive') and self.alive

def show_state(self):

"Display the agent's internal state. Subclasses should override."

print "I don't know how to show_state."

def display(self, canvas, x, y, width, height):

# Do we need this?

"Display an image of this Thing on the canvas."

pass

class Agent(Thing):

"""An Agent is a subclass of Thing 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 slot, .performance, which is a number giving

the performance measure of the agent in its environment."""

def __init__(self, program=None):

self.alive = True

self.bump = False

if program is None:

def program(percept):

return raw_input('Percept=%s; action? ' % percept)

assert callable(program)

self.program = program

def can_grab(self, thing):

"""Returns True if this agent can grab this thing.

Override for appropriate subclasses of Agent and Thing."""

return False

def TraceAgent(agent):

"""Wrap 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

#______________________________________________________________________________

def TableDrivenAgentProgram(table):

"""This agent selects an action based on the percept sequence.

It is practical only for tiny domains.

To customize it, provide as table a dictionary of all

{percept_sequence:action} pairs. [Fig. 2.7]"""

percepts = []

def program(percept):

percepts.append(percept)

action = table.get(tuple(percepts))

return action

return program

def RandomAgentProgram(actions):

"An agent that chooses an action at random, ignoring all percepts."

return lambda percept: random.choice(actions)

#______________________________________________________________________________

def SimpleReflexAgentProgram(rules, interpret_input):

"This agent takes action based solely on the percept. [Fig. 2.10]"

def program(percept):

state = interpret_input(percept)

rule = rule_match(state, rules)

action = rule.action

return action

return program

def ModelBasedReflexAgentProgram(rules, update_state):

"This agent takes action based on the percept and state. [Fig. 2.12]"

def program(percept):

program.state = update_state(program.state, program.action, percept)

rule = rule_match(program.state, rules)

action = rule.action

return action

program.state = program.action = None

return program

def rule_match(state, rules):

"Find the first rule that matches state."

for rule in rules:

if rule.matches(state):

return rule

#______________________________________________________________________________

loc_A, loc_B = (0, 0), (1, 0) # The two locations for the Vacuum world

def RandomVacuumAgent():

"Randomly choose one of the actions from the vacuum environment."

return Agent(RandomAgentProgram(['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 Agent(TableDrivenAgentProgram(table))

def ReflexVacuumAgent():

"A reflex agent for the two-state vacuum environment. [Fig. 2.8]"

def program((location, status)):

if status == 'Dirty': return 'Suck'

elif location == loc_A: return 'Right'

elif location == loc_B: return 'Left'

return Agent(program)

def ModelBasedVacuumAgent():

"An agent that keeps track of what locations are clean or dirty."

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'

return Agent(program)

#______________________________________________________________________________

class Environment(object):

"""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 .things and .agents (which is a subset

of .things). Each agent has a .performance slot, initialized to 0.

Each thing has a .location slot, even though some environments may not

need this."""

def __init__(self):

self.things = []

self.agents = []

def thing_classes(self):

return [] ## List of classes that can go into environment

def percept(self, agent):

"Return the percept that the agent sees at this point. (Implement this.)"

abstract

def execute_action(self, agent, action):

"Change the world to reflect this action. (Implement this.)"

abstract

def default_location(self, thing):

"Default location to place a new thing 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 any(agent.is_alive() for agent in 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 list_things_at(self, location, tclass=Thing):

"Return all things exactly at a given location."

return [thing for thing in self.things

if thing.location == location and isinstance(thing, tclass)]

def some_things_at(self, location, tclass=Thing):

"""Return true if at least one of the things at location

is an instance of class tclass (or a subclass)."""

return self.list_things_at(location, tclass) != []

def add_thing(self, thing, location=None):

"""Add a thing to the environment, setting its location. For

convenience, if thing is an agent program we make a new agent

for it. (Shouldn't need to override this."""

if not isinstance(thing, Thing):

thing = Agent(thing)

assert thing not in self.things, "Don't add the same thing twice"

thing.location = location or self.default_location(thing)

self.things.append(thing)

if isinstance(thing, Agent):

thing.performance = 0

self.agents.append(thing)

def delete_thing(self, thing):

"""Remove a thing from the environment."""

try:

self.things.remove(thing)

except ValueError, e:

print e

print " in Environment delete_thing"

print " Thing to be removed: %s at %s" % (thing, thing.location)

print " from list: %s" % [(thing, thing.location)

for thing in self.things]

if thing in self.agents:

self.agents.remove(thing)

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 things 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 things

that are held."""

def __init__(self, width=10, height=10):

super(XYEnvironment, self).__init__()

update(self, width=width, height=height, observers=[])

def things_near(self, location, radius=None):

"Return all things within radius of location."

if radius is None: radius = self.perceptible_distance

radius2 = radius * radius

return [thing for thing in self.things

if distance2(location, thing.location) <= radius2]

perceptible_distance = 1

def percept(self, agent):

"By default, agent perceives things within a default radius."

return [self.thing_percept(thing, agent)

for thing in self.things_near(agent.location)]

def execute_action(self, agent, action):

agent.bump = False

if action == 'TurnRight':

agent.heading = self.turn_heading(agent.heading, -1)

elif action == 'TurnLeft':

agent.heading = self.turn_heading(agent.heading, +1)

elif action == 'Forward':

self.move_to(agent, vector_add(agent.heading, agent.location))

# elif action == 'Grab':

# things = [thing for thing in self.list_things_at(agent.location)

# if agent.can_grab(thing)]

# if things:

# agent.holding.append(things[0])

elif action == 'Release':

if agent.holding:

agent.holding.pop()

def thing_percept(self, thing, agent): #??? Should go to thing?

"Return the percept for this thing."

return thing.__class__.__name__

def default_location(self, thing):

return (random.choice(self.width), random.choice(self.height))

def move_to(self, thing, destination):

"Move a thing to a new location."

thing.bump = self.some_things_at(destination, Obstacle)

if not thing.bump:

thing.location = destination

for o in self.observers:

o.thing_moved(thing)

def add_thing(self, thing, location=(1, 1)):

super(XYEnvironment, self).add_thing(thing, location)

thing.holding = []

thing.held = None

for obs in self.observers:

obs.thing_added(thing)

def delete_thing(self, thing):

super(XYEnvironment, self).delete_thing(thing)

# Any more to do? Thing holding anything or being held?

for obs in self.observers:

obs.thing_deleted(thing)

def add_walls(self):

"Put walls around the entire perimeter of the grid."

for x in range(self.width):

self.add_thing(Wall(), (x, 0))

self.add_thing(Wall(), (x, self.height-1))

for y in range(self.height):

self.add_thing(Wall(), (0, y))

self.add_thing(Wall(), (self.width-1, y))

def add_observer(self, observer):

"""Adds an observer to the list of observers.

An observer is typically an EnvGUI.

Each observer is notified of changes in move_to and add_thing,

by calling the observer's methods thing_moved(thing)

and thing_added(thing, loc)."""

self.observers.append(observer)

def turn_heading(self, heading, inc):

"Return the heading to the left (inc=+1) or right (inc=-1) of heading."

return turn_heading(heading, inc)

class Obstacle(Thing):

"""Something that can cause a bump, preventing an agent from

moving into the same square it's in."""

pass

class Wall(Obstacle):

pass

#______________________________________________________________________________

## Vacuum environment

class Dirt(Thing):

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):

super(VacuumEnvironment, self).__init__(width, height)

self.add_walls()

def thing_classes(self):

return [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.some_things_at(agent.location, Dirt),

'Dirty', 'Clean')

bump = if_(agent.bump, 'Bump', 'None')

return (status, bump)

def execute_action(self, agent, action):

if action == 'Suck':

dirt_list = self.list_things_at(agent.location, Dirt)

if dirt_list != []:

dirt = dirt_list[0]

agent.performance += 100

self.delete_thing(dirt)

else:

super(VacuumEnvironment, self).execute_action(agent, action)

if action != 'NoOp':

agent.performance -= 1

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):

super(TrivialVacuumEnvironment, self).__init__()

self.status = {loc_A: random.choice(['Clean', 'Dirty']),

loc_B: random.choice(['Clean', 'Dirty'])}

def thing_classes(self):

return [Wall, Dirt, ReflexVacuumAgent, RandomVacuumAgent,

TableDrivenVacuumAgent, ModelBasedVacuumAgent]

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, thing):

"Agents start in either location at random."

return random.choice([loc_A, loc_B])

#______________________________________________________________________________

## The Wumpus World

class Gold(Thing): pass

class Pit(Thing): pass

class Arrow(Thing): pass

class Wumpus(Agent): pass

class Explorer(Agent): pass

class WumpusEnvironment(XYEnvironment):

def __init__(self, width=10, height=10):

super(WumpusEnvironment, self).__init__(width, height)

self.add_walls()

def thing_classes(self):

return [Wall, Gold, Pit, Arrow, Wumpus, Explorer]

## 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 for steps. Return a list of (agent, average-score) tuples."""

envs = [EnvFactory() for i in range(n)]

return [(A, test_agent(A, steps, copy.deepcopy(envs)))

for A in AgentFactories]

def test_agent(AgentFactory, steps, envs):

"Return the mean score of running an agent in each of the envs, for steps"

def score(env):

agent = AgentFactory()

env.add_thing(agent)

env.run(steps)

return agent.performance

return mean(map(score, envs))

#_________________________________________________________________________

__doc__ += """

>>> a = ReflexVacuumAgent()

>>> 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_thing(ModelBasedVacuumAgent())

>>> e.run(5)

## Environments, and some agents, are randomized, so the best we can

## give is a range of expected scores. If this test fails, it does

## not necessarily mean something is wrong.

>>> envs = [TrivialVacuumEnvironment() for i in range(100)]

>>> def testv(A): return test_agent(A, 4, copy.deepcopy(envs))

>>> 7 < testv(ModelBasedVacuumAgent) < 11

True

>>> 5 < testv(ReflexVacuumAgent) < 9

True

>>> 2 < testv(TableDrivenVacuumAgent) < 6

True

>>> 0.5 < testv(RandomVacuumAgent) < 3

True

"""

#______________________________________________________________________________

# 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 EnvGUI(tk.Tk, object):

def __init__(self, env, title = 'AIMA GUI', cellwidth=50, n=10):

# Initialize window

super(EnvGUI, self).__init__()

self.title(title)

# Create components

canvas = EnvCanvas(self, env, cellwidth, n)

toolbar = EnvToolbar(self, env, canvas)

for w in [canvas, toolbar]:

w.pack(side="bottom", fill="x", padx="3", pady="3")

class EnvToolbar(tk.Frame, object):

def __init__(self, parent, env, canvas):

super(EnvToolbar, self).__init__(parent, relief='raised', bd=2)

# Initialize instance variables

self.env = env

self.canvas = canvas

self.running = False

self.speed = 1.0

# Create buttons and other controls

for txt, cmd in [('Step >', self.env.step),

('Run >>', self.run),

('Stop [ ]', self.stop),

('List things', self.list_things),

('List agents', self.list_agents)]:

tk.Button(self, text=txt, command=cmd).pack(side='left')

tk.Label(self, text='Speed').pack(side='left')

scale = tk.Scale(self, orient='h',

from_=(1.0), to=10.0, resolution=1.0,

command=self.set_speed)

scale.set(self.speed)

scale.pack(side='left')

def run(self):

print 'run'

self.running = True

self.background_run()

def stop(self):

print 'stop'

self.running = False

def background_run(self):

if self.running:

self.env.step()

# ms = int(1000 * max(float(self.speed), 0.5))

#ms = max(int(1000 * float(self.delay)), 1)

delay_sec = 1.0 / max(self.speed, 1.0) # avoid division by zero

ms = int(1000.0 * delay_sec) # seconds to milliseconds

self.after(ms, self.background_run)

def list_things(self):

print "Things in the environment:"

for thing in self.env.things:

print "%s at %s" % (thing, thing.location)

def list_agents(self):

print "Agents in the environment:"

for agt in self.env.agents:

print "%s at %s" % (agt, agt.location)

def set_speed(self, speed):

self.speed = float(speed)

aima-python/agents.py at aima3python2 · lap089/aima-python · GitHub

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