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)
pos = pygame.mouse.get_pos()
# Change the x/y screen coordinates to grid coordinates
column = pos[0] // (WIDTH + MARGIN)
row = pos[1] // (HEIGHT + MARGIN)
end_x = row
end_y = column
# Set that location to one
if grid[row][column].color == "red":
# print("Grid coordinates: ", row, column, "Node color: ", grid[row][column].color, "Node is passable: ", grid[row][column].passable,
# "Node name is: ", grid[row][column].name, "My neighbors are :", "My neighbors are: ", print_out_neighbor(grid[row][column].neighbor))
next
elif grid[row][column].color != 'grey':
grid[row][column].color = "green"
# print("Grid coordinates: ", row, column, "Node color: ", grid[row][column].color, "Node is passable: ", grid[row][column].passable,
# "Node name is: ", grid[row][column].name, "My neighbors are :", "My neighbors are: ", print_out_neighbor(grid[row][column].neighbor))
pathfinder.find_path(grid, start_x, start_y, end_x, end_y)
# Set the screen background
screen.fill(BLACK)
# Draw the grid
for row in range(ROWS):
for column in range(COLUMNS):
color = WHITE
if grid[row][column].color == "green":
color = GREEN
elif grid[row][column].color == "red":
color = RED
elif grid[row][column].color == "blue":
color = BLUE
elif grid[row][column].color == "yellow":
color = YELLOW
class RedAiPilot(Agent):
"""Controls the behaviors of the red rectangle in the game
RedAiPilot's decisions are based on its current_behavior state. The state is determined by the conditions of its
environment. A full list of behaviors can be found at the bottom of the red_ai_pilot module in the
PilotAgentBehavior class."""
def __init__(self, environment=None):
"""sets up details about the red rectangle as well as variables for A.I. behavior"""
super(RedAiPilot, self).__init__("red_ai_pilot", environment)
self.bot = Bot()
# set the rectangle's color
self.bot.image.fill(RED)
# set the starting position
self.bot.rect.x = RECTANGLE_STARTING_X
self.bot.rect.y = RECTANGLE_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 coordinates tell the agent how far away the blue player is
self.blue_coordinate = None
self.distance_from_blue_player = 0
self.current_position = 0
self.path_course = []
self.path_found = False
self.next_node_coordinates = None
# variables for A.I. behavior
self.winning = False
self.current_behavior = PilotAgentBehavior.FINDING_PATH
self.pathfinder = Pathfinder()
def act_out_decision(self):
if self.current_behavior == PilotAgentBehavior.FINDING_PATH:
self.path_course = self.pathfinder.find_path(self.blue_coordinate)
self.path_found = True
self.next_node_coordinates = (self.path_course[0].x,
self.path_course[0].y)
if self.current_behavior == PilotAgentBehavior.CHASING:
if not self.path_course:
self.path_found = False
return
self.find_next_node()
self.move_to_next_node()
if self.current_behavior is PilotAgentBehavior.SHOOTING:
self.determine_enemy_position()
self.shoot()
def check_distance_from_opponent(self):
self.update_coordinates()
self.distance_from_blue_player = \
abs(self.blue_coordinate[0] - self.red_coordinate[0]) + abs(self.blue_coordinate[1] - self.red_coordinate[1])
def determine_behavior(self):
self.check_distance_from_opponent()
if self.distance_from_blue_player > 100 and self.bot.hit_points > 50:
if not self.path_found:
self.current_behavior = PilotAgentBehavior.FINDING_PATH
else:
self.current_behavior = PilotAgentBehavior.CHASING
elif self.bot.hit_points > 50:
self.current_behavior = PilotAgentBehavior.SHOOTING
else:
self.current_behavior = PilotAgentBehavior.FLEEING
def determine_enemy_position(self):
if self.red_coordinate[0] + MARGIN >= self.blue_coordinate[
0] >= MARGIN - self.red_coordinate[0]:
# red player is above blue player
if self.red_coordinate[1] < self.blue_coordinate[1] - MARGIN:
self.current_position = PilotCurrentPosition.ABOVE
# red player is below blue player
elif self.red_coordinate[1] > self.blue_coordinate[1] + MARGIN:
self.current_position = PilotCurrentPosition.BELOW
elif self.red_coordinate[0] > self.blue_coordinate[0]:
self.current_position = PilotCurrentPosition.RIGHT
elif self.red_coordinate[0] < self.blue_coordinate[0]:
self.current_position = PilotCurrentPosition.LEFT
def find_next_node(self):
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):
self.determine_behavior()
self.act_out_decision()
def move_to_next_node(self):
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 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)
self.blue_coordinate = self.ask("blue_player_pilot", "blue_coordinate")
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.mouth = Mouth()
# self.ears = Ears()
self.wernicke_area = Wernicke_Area()
# 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 = pygame.sprite.Group()
self.current_behavior = BEHAVIOR_STATE.SCANNING
self.pathfinder = Pathfinder()
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 not self.detected_objects:
self.current_behavior = BEHAVIOR_STATE.SCANNING
elif self.inspecting:
self.current_behavior = BEHAVIOR_STATE.INSPECTING
elif self.detected_objects and not self.path_found:
self.current_behavior = BEHAVIOR_STATE.PATH_FINDING
elif self.path_found:
self.current_behavior = BEHAVIOR_STATE.APPROACHING
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 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):
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
pass
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):
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 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)
self.object_coordinates = self.ask("blue_player_pilot",
"blue_coordinate")
