agents.py

"""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

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

"""

from utils import *
import random, copy

# Additional modules needed for loading non-bitmap images
import Image # Python Imaging Library (PIL)
import ImageTk # PIL + Tk
#______________________________________________________________________________


class Object (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
        self.bump = 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

#______________________________________________________________________________

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.
        super(TableDrivenAgent, self).__init__()
        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):
        super(RandomAgent, self).__init__()
        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):
        super(ReflexVacuumAgent, self).__init__()
        def program((location, status)):
            if status == 'Dirty': return 'Suck'
            elif location == loc_A: return 'Right'
            elif location == loc_B: return 'Left'
        self.program = program

    image_file = "images/vacuum.png"
    
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):
        super(ModelBasedVacuumAgent, self).__init__()
        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 (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 .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 = []

    def object_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. 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."
        for agent in self.agents:
            if agent.is_alive(): return False
        return True

    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, obj, 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."""
	obj.location = location or self.default_location(obj)
	self.objects.append(obj)
	if isinstance(obj, Agent):
            obj.performance = 0
            self.agents.append(obj)
	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):
        super(XYEnvironment, self).__init__()
        self.width = width
        self.height = height
        #update(self, objects=[], agents=[], width=width, height=height)
        self.observers = []
        
    def objects_at(self, location): # promote to Environment?
        "Return all objects exactly at a given location."
        return [obj for obj in self.objects if obj.location == location]

    def find_at (self, oclass, location):
        """Return true if at least one of the objects at location
        is an instance of class oclass.

        'Is an instance' in the sense of 'isinstance',
        which is true if the object is an instance of a subclass of oclass."""
        
        return some(lambda obj: isinstance(obj, oclass),
                    self.objects_at(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(self, obj, destination):
        "Move an object to a new location."
        if self.find_at(Obstacle, destination):
            obj.bump = True
            # Nothing moved, so no report to observers
        else:
            obj.bump = False
            old_location = obj.location
            obj.location = destination
            print "Moving %s to %s -- now notify the observer somehow" % \
                  (obj, destination)
            # Report to observers
            for o in self.observers:
                o.object_moved(obj, old_location, obj.location)
        
    def add_object(self, obj, location=(1, 1)):
        super(XYEnvironment, self).add_object(obj, location)
        obj.holding = []
        obj.held = None
        self.objects.append(obj)
        # Report to observers
        for o in self.observers:
            o.object_added(obj, location)

    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 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_object,
        by calling the observer's methods object_moved(obj, old_loc, new_loc)
        and object_added(obj, loc)."""
        
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)]  

class Obstacle (Object):
    """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 (Object):

    image_file = "images/dirt.png"
    
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 object_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.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
        super(VacuumEnvironment, self).execute_action(agent, action)


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 object_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, object):
        "Agents start in either location at random."
        return random.choice([loc_A, loc_B])

#______________________________________________________________________________

class SimpleReflexAgent (Agent):
    """This agent takes action based solely on the percept. [Fig. 2.13]"""

    def __init__(self, rules, interpret_input):
        super(SimpleReflexAgent, self).__init__()
        def program(percept):
            state = interpret_input(percept)
            rule = rule_match(state, rules)
            action = rule.action
            return action
        self.program = program

class ReflexAgentWithState (Agent):
    """This agent takes action based on the percept and state. [Fig. 2.16]"""

    def __init__(self, rules, udpate_state):
        super(ReflexAgentWithState, self).__init__()
        state, action = None, None
        def program(percept):
            state = update_state(state, action, percept)
            rule = rule_match(state, rules)
            action = rule.action
            return action
        self.program = program

#______________________________________________________________________________
## 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):

    def __init__(self, width=10, height=10):
        super(WumpusEnvironment, self).__init__(width, height)
        self.add_walls()

    def object_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"
    total = 0
    for env in envs:
        agent = AgentFactory()
        env.add_object(agent)
        env.run(steps)
        total += agent.performance
    return float(total)/len(envs)

#_________________________________________________________________________

_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)

## 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)) 
testv(ModelBasedVacuumAgent)
(7 < _ < 11) ==> True
testv(ReflexVacuumAgent)
(5 < _ < 9) ==> True
testv(TableDrivenVacuumAgent)
(2 < _ < 6) ==> True
testv(RandomVacuumAgent)
(0.5 < _ < 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 = 0
        self.delay = 1.0

        # Create buttons and other controls
        
        for txt, cmd in [('Step >', self.env.step), ('Run >>', self.run),
                         ('Stop [ ]', self.stop)]:
            tk.Button(self, text=txt, command=cmd).pack(side='left')
        tk.Label(self, text='Delay').pack(side='left')
        scale = tk.Scale(self, orient='h', from_=0.0, to=10, resolution=0.5,
                         command=lambda d: setattr(parent, 'delay', d))
        scale.set(self.delay)
        scale.pack(side='left')

    def run(self):
        print 'run'
        self.running = 1
        self.background_run()

    def stop(self):
        print 'stop'
        self.running = 0

    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)
        

class EnvCanvas (tk.Canvas, object):

    def __init__ (self, parent, env, cellwidth, n):
        canvwidth = cellwidth * n # (cellwidth + 1 ) * n
        canvheight = cellwidth * n # (cellwidth + 1) * n
        super(EnvCanvas, self).__init__(parent, background="white",
                                        width=canvwidth, height=canvheight)

        # Initialize instance variables
        
        self.env = env
        self.cellwidth = cellwidth
        self.n = n
        print "cellwidth, canvwidth, camvheight = %d, %d, %d" % \
              (self.cellwidth, canvwidth, canvheight)

        # Draw the gridlines
        
        if cellwidth:
            for i in range(0, n+1):
                self.create_line(0, i*cellwidth, n*cellwidth, i*cellwidth)
                self.create_line(i*cellwidth, 0, i*cellwidth, n*cellwidth)
                self.pack(expand=1, fill='both')            
        self.pack()

        # Set up image dictionary.

        # Ugly hack: we need to keep a reference to each ImageTk.PhotoImage,
        # or it will be garbage collected.  This dictionary maps image files
        # that have been opened to their PhotoImage objects
        self.images = {}

        # Bind canvas events.
        
        #self.bind('<Button-1>', self.user_left) ## What should this do?
        #self.bind('<Button-2>', self.user_edit_objects)        
        self.bind('<Button-3>', self.user_add_object)

    def get_image (self, file):
        """Try to find the image in the images dictionary.
        If it's not there, open the file and create it, and stick
        it in the dictionary.  Return the image in a form usable
        by the canvas."""
        if self.images.has_key(file):
            tk_image = self.images[file]
        else:
            pil_image = Image.open(file)
            tk_image = ImageTk.PhotoImage(pil_image)
            self.images[file] = tk_image
        return tk_image

    def user_left(self, event):
        print 'left at %d, %d' % self.event_cell(event)

    def user_edit_objects(self, event):
        """Choose an object within radius and edit its fields."""
        pass

    def user_add_object(self, event):
        """Pops up a menu of Object classes; you choose the
        one you want to put in this square."""
        cell = self.event_cell(event)
        xy = self.cell_topleft_xy(cell)
        menu = tk.Menu(self, title='Edit (%d, %d)' % cell)
        # Generalize object classes available,
        # and why is self.run the command?
        #for (txt, cmd) in [('Wumpus', self.run), ('Pit', self.run)]:
        #    menu.add_command(label=txt, command=cmd)
        obj_classes = self.env.object_classes()

        def class_cmd (oclass):
            def cmd ():
                obj = oclass()
                self.env.add_object(obj, cell)
                # what about drawing it on the canvas?
                print "Drawing object %s at %s %s" % (obj, cell, xy)
                tk_image = self.get_image(oclass.image_file)
                self.canvas.create_image(xy, anchor="nw", image=tk_image)
            return cmd

        for oclass in obj_classes:
            menu.add_command(label=oclass.__name__, command=class_cmd(oclass))
            
        menu.tk_popup(event.x + self.winfo_rootx(),
                      event.y + self.winfo_rooty())
        
    def event_cell (self, event):
        return self.xy_cell((event.x, event.y))

    def xy_cell (self, (x, y)):
        """Given an (x, y) on the canvas, return the row and column
        of the cell containing it."""
        w = self.cellwidth
        return x / w, y / w
    
    def cell_topleft_xy (self, (row, column)):
        """Given a (row, column) tuple, return the (x, y) coordinates
        of the cell(row, column)'s top left corner."""

        w = self.cellwidth
        return w * row, w * column


def test_gui ():
    v = VacuumEnvironment()
    w = EnvGUI(v)
    a = ModelBasedVacuumAgent()
    v.add_object(TraceAgent(a))
    v.run(5)