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")