class brain(Agent):
"""The driver Agent of the program, manages all other agents. The consciousness of NEO"""
def __init__(self, environment):
"""default constructor"""
super(brain, self).__init__("brain", environment)
self.CURRENT_STATE = BEHAVIOR_STATE.SCANNING
self.facing_direction = "RIGHT"
self.finished = False
self.position = 100
self.sql_statement = None
self.list_of_objects = None
self.current_object_name = None
self.uninspected_objects = []
self.eyes = Eyes()
self.hands = Hands()
self.memory = Memory()
self.mouth = Mouth()
self.legs = Legs()
def determine_object_name(self):
self.current_object_name = self.uninspected_objects[0].name
self.uninspected_objects.pop(0)
def find_objects(self, query):
self.sql_statement = "SELECT OBJECT_NAME FROM OBJECTS WHERE OBJECT_COLOR = '" + query + "';"
self.memory.recall_objects()
self.list_of_objects = self.ask("memory", "short_term_memory")
self.mouth.list_similar_objects()
def scan_room(self):
self.eyes.scan_area()
self.mouth.report_visible_objects()
def run_learning_program(self):
if self.CURRENT_STATE == BEHAVIOR_STATE.SCANNING:
self.scan_room()
self.uninspected_objects = self.ask("eyes", "visible_object_list")
self.CURRENT_STATE = BEHAVIOR_STATE.APPROACHING
elif self.CURRENT_STATE == BEHAVIOR_STATE.APPROACHING:
while self.position != self.uninspected_objects[0].position:
self.legs.walk()
self.position = self.ask("legs", "position")
self.mouth.state_current_position()
self.CURRENT_STATE = BEHAVIOR_STATE.INSPECTING
elif self.CURRENT_STATE == BEHAVIOR_STATE.INSPECTING:
self.hands.pick_up_object()
self.eyes.look_at_object()
self.determine_object_name()
self.memory.memorize()
if self.uninspected_objects:
self.CURRENT_STATE = BEHAVIOR_STATE.APPROACHING
else:
self.finished = True
else:
self.CURRENT_STATE = BEHAVIOR_STATE.FINISHED
self.finished = True
def __init__(self, environment=None):
"""sets up details about the red rectangle as well as variables for A.I. behavior"""
super(NEO, self).__init__("neo", environment)
# the bot object represents NEO's physical body
# it performs the tasks as directed by the body part classes below
self.bot = Bot()
# set the bot's image and angle
self.bot.image = pygame.Surface([16, 16])
self.bot.image = pygame.transform.scale(pygame.image.load('Neo.png'),
(25, 25))
self.original_image = self.bot.image.copy()
self.angle = 90
# set the starting position
self.bot.rect.x = NEO_STARTING_X
self.bot.rect.y = NEO_STARTING_Y
# place the coordinates of our rectangle in a tuple to update the game manager
self.red_coordinate = (self.bot.rect.x, self.bot.rect.y)
self.rect = self.bot.rect
# these different body part classes help modularize the various functions NEO performs
self.eyes = Eyes()
self.hands = Hands()
self.legs = Legs()
self.memory = Memory()
self.memory.create_object_memory()
# self.mouth = Mouth()
# self.ears = Ears()
self.wernicke_area = Wernicke_Area()
self.current_object = None
# these coordinates tell the agent how far away the object is
self.object_coordinates = None
self.distance_from_object = 0
self.current_position = 0
self.path_course = []
self.path_found = False
self.next_node_coordinates = None
# variables for A.I. behavior
self.inspecting = False
self.detected_objects = []
self.uninspected_objects = []
self.current_behavior = BEHAVIOR_STATE.SCANNING
self.pathfinder = Pathfinder()
self.running_training = True
#search variables
self.rotating_counter = 0
self.location_coordinates = self.ask("memory", "location_coordinates")
self.sql_statement = None
def __init__(self, environment):
"""default constructor"""
super(brain, self).__init__("brain", environment)
self.CURRENT_STATE = BEHAVIOR_STATE.SCANNING
self.facing_direction = "RIGHT"
self.finished = False
self.position = 100
self.sql_statement = None
self.list_of_objects = None
self.current_object_name = None
self.uninspected_objects = []
self.eyes = Eyes()
self.hands = Hands()
self.memory = Memory()
self.mouth = Mouth()
self.legs = Legs()
def __init__(self, environment):
"""default constructor"""
super(brain, self).__init__("brain", environment)
self.CURRENT_STATE = BEHAVIOR_STATE.SCANNING
self.facing_direction = "RIGHT"
self.finished = False
self.position = 100
self.eyes = Eyes()
self.hands = Hands()
self.memory = Memory()
self.mouth = Mouth()
class NEO(Agent):
"""Controls the behaviors of the NEO bot
NEO has four main tasks:
1. scan the room for new objects,
2. approach discovered objects,
3. inspect objects and gather info about their attributes,
4. accept queries from the user to test its memory of objects.
NEO performs these tasks by using the various body part classes located in the neo_body directory. This NEO
class essentially serves as the 'brain' class and passes commands to all other body parts as needed. NEO and every
body part class are subclasses of the Agent class, which allows them to safely share info with each other using
Agent Oriented Programming methods. This allows us to easily add new parts or replace parts altogether."""
def __init__(self, environment=None):
"""sets up details about the red rectangle as well as variables for A.I. behavior"""
super(NEO, self).__init__("neo", environment)
# the bot object represents NEO's physical body
# it performs the tasks as directed by the body part classes below
self.bot = Bot()
# set the bot's image and angle
self.bot.image = pygame.Surface([16, 16])
self.bot.image = pygame.transform.scale(pygame.image.load('Neo.png'),
(25, 25))
self.original_image = self.bot.image.copy()
self.angle = 90
# set the starting position
self.bot.rect.x = NEO_STARTING_X
self.bot.rect.y = NEO_STARTING_Y
# place the coordinates of our rectangle in a tuple to update the game manager
self.red_coordinate = (self.bot.rect.x, self.bot.rect.y)
self.rect = self.bot.rect
# these different body part classes help modularize the various functions NEO performs
self.eyes = Eyes()
self.hands = Hands()
self.legs = Legs()
self.memory = Memory()
self.memory.create_object_memory()
# self.mouth = Mouth()
# self.ears = Ears()
self.wernicke_area = Wernicke_Area()
self.current_object = None
# these coordinates tell the agent how far away the object is
self.object_coordinates = None
self.distance_from_object = 0
self.current_position = 0
self.path_course = []
self.path_found = False
self.next_node_coordinates = None
# variables for A.I. behavior
self.inspecting = False
self.detected_objects = []
self.uninspected_objects = []
self.current_behavior = BEHAVIOR_STATE.SCANNING
self.pathfinder = Pathfinder()
self.running_training = True
#search variables
self.rotating_counter = 0
self.location_coordinates = self.ask("memory", "location_coordinates")
self.sql_statement = None
def act_out_decision(self):
pass
def check_distance_from_object(self):
self.update_coordinates()
self.distance_from_object = \
abs(self.object_coordinates[0] - self.red_coordinate[0]) + \
abs(self.object_coordinates[1] - self.red_coordinate[1])
def determine_behavior(self):
if self.inspecting:
self.current_behavior = BEHAVIOR_STATE.INSPECTING
elif self.uninspected_objects and not self.path_found:
self.current_behavior = BEHAVIOR_STATE.PATH_FINDING
self.current_object = self.uninspected_objects[0]
elif self.path_found:
self.current_behavior = BEHAVIOR_STATE.APPROACHING
else:
self.current_behavior = BEHAVIOR_STATE.SCANNING
def determine_object_position(self):
""""used to determine which direction the red player should turn to face object"""
if self.red_coordinate[0] + MARGIN >= self.object_coordinates[
0] >= MARGIN - self.red_coordinate[0]:
# red player is above blue player
if self.red_coordinate[1] < self.object_coordinates[1] - MARGIN:
self.current_position = PilotCurrentPosition.ABOVE
# red player is below blue player
elif self.red_coordinate[1] > self.object_coordinates[1] + MARGIN:
self.current_position = PilotCurrentPosition.BELOW
# red player is right of blue
elif self.red_coordinate[0] > self.object_coordinates[0]:
self.current_position = PilotCurrentPosition.RIGHT
# red player is left of blue
elif self.red_coordinate[0] < self.object_coordinates[0]:
self.current_position = PilotCurrentPosition.LEFT
def filter_detected_objects(self):
"""Decides whether detected objects(currently just 1 at a time) have been inspected before"""
for obj in self.detected_objects:
self.sql_statement = "SELECT * FROM objects WHERE object_x_pos = " + str(obj.x) + \
" AND object_y_pos = " + str(obj.y)
self.memory.recall_objects()
if self.ask("memory", "short_term_memory"):
self.detected_objects.remove(obj)
else:
self.uninspected_objects += [obj]
self.detected_objects.remove(obj)
def find_next_node(self):
"""finds the closest node in our path and removes nodes once they are reached"""
if not (1 <= abs(self.rect.centerx - self.next_node_coordinates[0]) or
1 <= abs(self.rect.centery - self.next_node_coordinates[1])):
self.path_course.pop(0)
if self.path_course:
self.next_node_coordinates = (self.path_course[0].x,
self.path_course[0].y)
def is_healthy(self):
return self.hit_points > 50
def make_decision(self):
"""Acts out decisions based on the current behavior state"""
if not self.running_training:
self.filter_detected_objects()
self.determine_behavior()
if self.current_behavior == BEHAVIOR_STATE.SCANNING:
self.legs.rotate()
self.angle = self.ask("legs", "angle")
self.eyes.scan_room()
# _thread.start_new_thread(self.mouth.speak, ("Scanning Room",))
elif self.current_behavior == BEHAVIOR_STATE.PATH_FINDING:
self.path_course = self.pathfinder.find_path(
self.object_coordinates)
self.path_found = True
self.next_node_coordinates = (self.path_course[0].x,
self.path_course[0].y)
elif self.current_behavior == BEHAVIOR_STATE.APPROACHING:
if not self.path_course:
self.path_found = False
self.inspecting = True
return
self.find_next_node()
self.move_to_next_node()
elif self.current_behavior == BEHAVIOR_STATE.INSPECTING:
# if not self.mouth.inspection_message_spoken:
# self.mouth.stopSentence()
# _thread.start_new_thread(self.mouth.speak, ("Inspecting Object",))
# self.mouth.inspection_message_spoken = True
self.eyes.look_at_object()
self.hands.pick_up_object()
self.memory.memorize()
self.inspecting = False
self.uninspected_objects.remove(self.current_object)
self.current_object = None
else:
self.run_training()
def move_to_next_node(self):
"""tells the bot which direction to move to reach the next node in its path"""
if self.next_node_coordinates[0] < self.rect.centerx:
self.bot.move(-2, 0)
elif self.next_node_coordinates[0] > self.rect.centerx:
self.bot.move(2, 0)
# up and down
if self.next_node_coordinates[1] < self.rect.centery:
self.bot.move(0, -2)
elif self.next_node_coordinates[1] > self.rect.centery:
self.bot.move(0, 2)
# def rotate(self): Use the one in legs.py instead
# self.angle += .5
# key = pygame.key.get_pressed()
# if key[pygame.K_LEFT]:
# self.angle += 4
# if key[pygame.K_RIGHT]:
# self.angle -= 4
# # self.angle += 2
# self.angle %= 360
# self.bot.angle_facing = self.angle
# self.bot.update_dx_dy()
#
# rect_center = self.bot.image.get_rect()
# self.bot.image = pygame.transform.rotozoom(self.original_image, self.angle, 1)
# rot_rect = rect_center.copy()
# rot_rect.center = self.bot.image.get_rect().center
# self.bot.image = self.bot.image.subsurface(rot_rect).copy()
def run_training(self):
if self.current_behavior != BEHAVIOR_STATE.PATH_FINDING:
key = pygame.key.get_pressed()
if key[pygame.K_LEFT]:
self.legs.walk(-2, 0)
if key[pygame.K_RIGHT]:
self.legs.walk(2, 0)
if key[pygame.K_UP]:
self.legs.walk(0, -2)
if key[pygame.K_DOWN]:
self.legs.walk(0, 2)
if key[pygame.K_SPACE]:
location_name = input("Enter a name for this location:")
self.memory.save_location(location_name)
if key[pygame.K_c]:
command = input("Enter a command to run")
self.wernicke_area.parse_command(command)
# location_coordinates = self.ask("wernicke_area", "location_coordinates")
self.update_coordinates()
self.current_behavior = self.ask("wernicke_area",
"next_behavior")
self.current_behavior = BEHAVIOR_STATE(7)
# to do: add division between tasks
# self.path_course = self.pathfinder.find_path(location_coordinates)
# self.current_behavior = BEHAVIOR_STATE.PATH_FINDING
# print(self.path_course)
elif self.current_behavior == BEHAVIOR_STATE.PATH_FINDING:
if not self.path_course:
self.path_found = False
self.current_behavior = BEHAVIOR_STATE.TRAINING
return
else:
self.next_node_coordinates = (self.path_course[0].x,
self.path_course[0].y)
self.find_next_node()
self.move_to_next_node()
def search_for(self):
self.legs.rotate()
self.rotating_counter += 1
self.angle = self.ask("legs", "angle")
if self.rotating_counter > 360:
self.rotating_counter = 0
self.current_behavior = BEHAVIOR_STATE.SWITCHING_ROOMS
else:
self.eyes.scan_room()
self.current_behavior = BEHAVIOR_STATE.SEARCHING
def update_search(self):
if self.current_behavior == BEHAVIOR_STATE.SEARCHING:
self.search_for()
elif self.current_behavior == BEHAVIOR_STATE.SWITCHING_ROOMS:
self.go_to_room()
def go_to_room(self):
if self.path_course is None:
self.path_course = self.pathfinder.find_path(
self.location_coordinates[0])
self.location_coordinates.pop(0)
else:
self.next_node_coordinates = (self.path_course[0].x,
self.path_course[0].y)
self.move_to_next_node()
def setup_bot_map(self):
self.bot.wall_list = self.environment.get_object("wall_list")
def shoot(self):
if self.bot.reloaded():
bullet_list = self.environment.get_object("bullet_list")
if self.current_position == PilotCurrentPosition.LEFT:
bullet_list.add(self.bot.shoot_right())
elif self.current_position == PilotCurrentPosition.RIGHT:
bullet_list.add(self.bot.shoot_left())
elif self.current_position == PilotCurrentPosition.ABOVE:
bullet_list.add(self.bot.shoot_down())
elif self.current_position == PilotCurrentPosition.BELOW:
bullet_list.add(self.bot.shoot_up())
def update_coordinates(self):
self.red_coordinate = (self.rect.x, self.rect.y)