TestProject | Python Fiddle

# Skeleton code taken from github.com/aimacode/aima-python

from statistics import mean

import random
import copy
import collections

# ______________________________________________________________________________

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 '<{}>'.format(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.")

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
        self.holding = []
        self.performance = 0
        if program is None:
            def program(percept):
                return eval(input('Percept={}; action? ' .format(percept)))
        assert isinstance(program, collections.Callable)
        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

# TODO: Learn how this works
#     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('{} perceives {} and does {}'.format(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. [Figure 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. [Figure 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. [Figure 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, loc_C = (0, 0), (1, 0), (2, 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():
    "[Figure 2.3]"
    table = {((loc_A, 'Clean'),): 'Right',
             ((loc_A, 'Dirty'),): 'Suck',
             ((loc_B, 'Clean'),): 'Right',
             ((loc_B, 'Clean'),): 'Left',
             ((loc_B, 'Dirty'),): 'Suck',
             ((loc_C, 'Clean'),): 'Left',
             ((loc_C, '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 simple reflex agent for the three-state vacuum environment. [Figure 2.8]"
    # Augmented to include the 3-room problem environment
    def program(percept):
        location, status = percept
        if status == 'Dirty':
            print(percept, 'Suck')
            return 'Suck'
        elif location == loc_A:
            print(percept, 'Right')
            return 'Right'
        elif location == loc_B:
            decision = random.randint(0,1)
            if decision == 0:
                print(percept, 'Left')
                return 'Left'
            elif decision == 1:
                print(percept, 'Right')
                return 'Right'
        elif location == loc_C:
            print(percept, 'Left')
            return 'Left'
    return Agent(program)

def AllSeeingReflexVaccumAgent():
    "more powerful sensors"
    def program(percept):
        # Now reveals all locations and their statuses to agent(s)
        location, status = percept
        if status[loc_A] == status[loc_B] == status[loc_C] == 'Clean' :
            print(percept, 'NoOp')
            return 'NoOp'
        elif status[location] == 'Dirty':
            print(percept, 'Suck')
            return 'Suck'
        elif location==loc_A and (status[loc_B] == 'Dirty' or status[loc_C] == 'Dirty') :
            print(percept, 'Right')
            return 'Right'
        elif location==loc_B and status[loc_A] == 'Dirty':
            print(percept, 'Left')
            return 'Left'
        elif location==loc_B and status[loc_C] == 'Dirty':
            print(percept, 'Right')
            return 'Right'
        elif location==loc_C and (status[loc_B] == 'Dirty' or status[loc_A] == 'Dirty') :
            print(percept, 'Left')
            return 'Left'
        
    return Agent(program)

def BasicVaccumAgent(): 
    #Cannot see location, cannot store status, without randomizer
    def program(percept):
        if percept == 'Dirty':
            print(percept, 'Suck')
            return 'Suck'
        else:
            # Decide random direction without randomizer inclusion.
            print(percept, 'Right')
            return 'Right'
    return Agent(program)

def SemiBasicVaccumAgent():
    #Cannot see location, cannot store status, with randomizer
    def program(percept):
        if percept == 'Dirty':
            print(percept, 'Suck')
            return 'Suck'
        else:
            choice = random.randint(0,1)
            if choice == 0:
                print(percept, 'Left')
                return 'Left'
            else:
                print(percept, 'Right')
                return 'Right'
    return Agent(program)

def ModelBasedVacuumAgent():
    "An agent that keeps track of what locations are clean or dirty."
    model = {loc_A: None, loc_B: None, loc_C: None}

def program(percept):
        "Same as ReflexVacuumAgent, except if everything is clean, do NoOp."
        # Augmented to include the 3-room format
        location, status = percept
        model[location] = status  # Update the model here
        if model[loc_A] == model[loc_B] == model[loc_C] == 'Clean':
            print(percept, 'NoOp')
            return 'NoOp'
        elif status == 'Dirty':
            print(percept, 'Suck')
            return 'Suck'
        elif location == loc_A and status == 'Clean':
            print(percept, 'Right')
            return 'Right'
        elif location == loc_B and status == 'Clean':
            if model[loc_A] == 'Clean':
                print(percept, 'Right')
                return 'Right'
            elif model[loc_C] == 'Clean':
                print(percept, 'Left')
                return 'Left'
            else:
                position = random.randint(0,1)
                if position == 0:
                    print(percept, 'Left')
                    return 'Left'
                elif position == 1:
                    print(percept, 'Right')
                    return 'Right'
        elif location == loc_C and status == 'Clean':
            print(percept,'Left')
            return 'Left'
        elif location == loc_A and (model[loc_B] or model[loc_C] == 'Dirty'):
            print(percept, 'Right')
            return 'Right'
        elif location == loc_B and (model[loc_A] or model[loc_C] == 'Dirty'):
            if model[loc_A] == 'Dirty':
                print(percept, 'Left')
                return 'Left'
            elif model[loc_C] == 'Dirty':
                print(percept, 'Right')
                return 'Right'
        elif location == loc_C and (model[loc_A] or model[loc_B] == 'Dirty'):
            print(percept, 'Left')
            return 'Left'

return Agent(program)

class Dirt(Thing):
    pass

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.)
        '''
        raise NotImplementedError

def execute_action(self, agent, action):
        "Change the world to reflect this action. (Implement this.)"
        raise NotImplementedError

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 = []
            for agent in self.agents:
                if agent.alive:
                    actions.append(agent.program(self.percept(agent)))
                else:
                    actions.append("")
            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 if location is not None else 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 as e:
            print(e)
            print("  in Environment delete_thing")
            print("  Thing to be removed: {} at {}" .format(thing, thing.location))
            print("  from list: {}" .format([(thing, thing.location) for thing in self.things]))
        if thing in self.agents:
            self.agents.remove(thing)

class TrivialVacuumEnvironment(Environment):
    """This environment has three locations, A, B, and C. 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']),
                       loc_C: random.choice(['Clean', 'Dirty'])}
        print('The environment for this attempt is:')
        print(self.status)

def thing_classes(self):
        return [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':
            if agent.location == loc_B:
                agent.location = loc_C
            elif agent.location == loc_A:
                agent.location = loc_B
            agent.performance -= 1
        elif action == 'Left':
            if agent.location == loc_B:
                agent.location = loc_A
            elif agent.location == loc_C:
                agent.location = loc_B
            agent.performance -= 1
        elif action == 'Suck':
            if self.status[agent.location] == 'Dirty':
                agent.performance += 10
            self.status[agent.location] = 'Clean'
        for floorStatus in self.status:
            if floorStatus == 'Dirty':
                agent.performance -= 2

def default_location(self, thing):
        "Agents start in a location at random."
        return random.choice([loc_A, loc_B, loc_C])

class AllSeeingTrivialVaccumEnvironment(TrivialVacuumEnvironment):
    # Same as TrivialVaccumEnviornment, except gives all locations as well as statuses

def percept(self, agent):
        "Returns the agent's location, and the location status (Dirty/Clean)."
        return (agent.location, self.status)

class BasicVaccumEnvironment(TrivialVacuumEnvironment):
    # Same as TrivialVaccumEnvironment; agent knows status yet no location
    
    def percept(self, agent):
        return(self.status[agent.location])

def run_times(EnvFactory, AgentFactory, steps, runs):
    for i in range(runs):
        env = EnvFactory()
        agent = AgentFactory()
        env.add_thing(agent)
        env.run(steps)
        score = str(agent.performance)
        print(score)

"""Case 1(a) - The Simple Reflex Agent - Has rules, but no states"""
print("The Simple Reflex Agent Tests:")
run_times(TrivialVacuumEnvironment, ReflexVacuumAgent, 100, 4)

"""Case 1(b) - The Reflex Agent - Has rules, has states"""
print("The Reflex Agent Tests:")
run_times(TrivialVacuumEnvironment, ModelBasedVacuumAgent, 100, 4)

"""Case 1(c) - All-Seeing Reflex Agent - Has rules, has all states including env"""
print("The All-Seeing Reflex Agent Tests:")
run_times(AllSeeingTrivialVaccumEnvironment, AllSeeingReflexVaccumAgent, 100, 4)

"""Case 3(a) - Basic Agent - Cannot see location, does not remember states, and no randomizer"""
print("The Basic Agent Tests:")
run_times(BasicVaccumEnvironment, BasicVaccumAgent, 100, 4)

"""Case 3(b) - Semi-Basic Agent - Cannot see location, does not remember states, has randomizer"""
print("The Semi-Basic Agent Tests:")
run_times(BasicVaccumEnvironment, SemiBasicVaccumAgent, 100, 4)

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