Artificial Intelligence: A Modern Approach

AIMA Python file:

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

The class hierarchies are as follows:

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

Environment ## An environment holds objects, runs simulations

EnvFrame ## A graphical representation of the Environment


from utils import *
import random, copy

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): """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
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. Agent.__init__(self) 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): Agent.__init__(self) 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): Agent.__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): Agent.__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
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." 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, 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)
SimpleReflexAgent(Agent): """This agent takes action based solely on the percept. [Fig. 2.13]""" def __init__(self, rules, interpret_input): Agent.__init__(self) 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): Agent.__init__(self) 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): 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 ...
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) 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())) ## 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
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 >>',, ('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',, ('Pit',]: 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); '''

AI: A Modern Approach by Stuart Russell and Peter NorvigModified: Jul 18, 2005