Module `chillow.service.ai.pathfinding_search_tree_ai`

Expand source code

from typing import List, Tuple, Optional
from multiprocessing import Value

from chillow.service.ai.pathfinding_ai import PathfindingAI
from chillow.service.ai.search_tree_ai import SearchTreeAI
from chillow.model.action import Action
from chillow.model.game import Game
from chillow.model.player import Player


class PathfindingSearchTreeAI(PathfindingAI, SearchTreeAI):
    """This AI combines the PathfindingAI and the SearchTreeAI by favoring the former.

    Therefore it ranks all actions based on the PathfindingAI and checks finds the first surviving action with the
    SearchTreeAI.

    Attributes:
        player: The player associated with this AI.
    """

    def __init__(self, player: Player, max_speed: int, count_paths_to_check: int, depth: int,
                 paths_tolerance: float = 0.75, distance_to_check: int = 0):
        """Creates a new object of the PathfindingSearchTreeAI.

        Args:
            player: The player assigned to the AI.
            max_speed: The maximum speed the AI can reach.
            count_paths_to_check: The number of paths used to avoid dead ends.
            depth: Depth pre-calculating actions.
            paths_tolerance: A tolerance, whereby more than just the best action is calculated. Actions which are
                worse, but within this tolerance, are also considered.
                depth: Number of player actions that are looked into the future.
            distance_to_check: Distance an enemy player is allowed to be at maximum distance, so that he is taken into
                account in the calculations.
        """
        PathfindingAI.__init__(self, player, max_speed, count_paths_to_check)
        SearchTreeAI.__init__(self, player, depth, max_speed, distance_to_check=distance_to_check)
        self.__paths_tolerance = paths_tolerance

    def get_information(self) -> str:
        """See base class."""
        return "max_speed=" + str(self._max_speed) \
               + ", paths_tolerance=" + str(self.__paths_tolerance) \
               + ", count_paths_to_check=" + str(self._get_count_paths_to_check()) \
               + ", depth=" + str(self._get_depth()) \
               + ", distance_to_check=" + str(self._get_distance_to_check())

    def create_next_action(self, game: Game, return_value: Value):
        """See base class."""
        self._turn_ctr += 1

        pathfinding_actions = self.create_next_actions_ranked(game)
        self.set_best_action(pathfinding_actions, [], return_value)
        search_tree_actions = self.create_all_next_surviving_actions(game)
        self.set_best_action(pathfinding_actions, search_tree_actions, return_value)

    def set_best_action(self, pathfinding_actions: List[Tuple[Action, int]], search_tree_actions: List[Action],
                        return_value: Value):
        """Saves the best action from the list of actions from PathfindingAI and SearchTreeAI.

        Args:
            pathfinding_actions: List of actions calculated by PathfindingAI.
            search_tree_actions: List of actions calculated by SearchTreeAI
            return_value: Object to save the result of the calculation.
        """
        best_action = self.get_best_action(pathfinding_actions, search_tree_actions)
        return_value.value = best_action.get_index() if best_action is not None else return_value.value

    def get_best_action(self, pathfinding_actions: List[Tuple[Action, int]],
                        search_tree_actions: List[Action]) -> Optional[Action]:
        """Calculates the best action from the list of actions from PathfindingAI and SearchTreeAI.

        Args:
            pathfinding_actions: List of actions calculated by PathfindingAI.
            search_tree_actions: List of actions calculated by SearchTreeAI

        Returns:
            Best action if there is any.
        """
        if search_tree_actions is None or len(search_tree_actions) == 0:
            if pathfinding_actions is not None and len(pathfinding_actions) > 0:
                return pathfinding_actions[0][0]
            return None
        elif pathfinding_actions is None or len(pathfinding_actions) == 0:
            return search_tree_actions[0]

        for (action, possible_paths) in pathfinding_actions:
            if action in search_tree_actions:
                if possible_paths == pathfinding_actions[0][1]:
                    return action  # best path and surviving guaranteed
                elif possible_paths >= pathfinding_actions[0][1] * self.__paths_tolerance:
                    return action  # good path and surviving guaranteed
                else:
                    break

        return pathfinding_actions[0][0]

Classes

class PathfindingSearchTreeAI (player: Player, max_speed: int, count_paths_to_check: int, depth: int, paths_tolerance: float = 0.75, distance_to_check: int = 0)

This AI combines the PathfindingAI and the SearchTreeAI by favoring the former.

Therefore it ranks all actions based on the PathfindingAI and checks finds the first surviving action with the SearchTreeAI.

Attributes

player: The player associated with this AI.

Creates a new object of the PathfindingSearchTreeAI.

Args

player: The player assigned to the AI.
max_speed: The maximum speed the AI can reach.
count_paths_to_check: The number of paths used to avoid dead ends.
depth: Depth pre-calculating actions.
paths_tolerance: A tolerance, whereby more than just the best action is calculated. Actions which are worse, but within this tolerance, are also considered. depth: Number of player actions that are looked into the future.
distance_to_check: Distance an enemy player is allowed to be at maximum distance, so that he is taken into account in the calculations.

Expand source code

class PathfindingSearchTreeAI(PathfindingAI, SearchTreeAI):
    """This AI combines the PathfindingAI and the SearchTreeAI by favoring the former.

    Therefore it ranks all actions based on the PathfindingAI and checks finds the first surviving action with the
    SearchTreeAI.

    Attributes:
        player: The player associated with this AI.
    """

    def __init__(self, player: Player, max_speed: int, count_paths_to_check: int, depth: int,
                 paths_tolerance: float = 0.75, distance_to_check: int = 0):
        """Creates a new object of the PathfindingSearchTreeAI.

        Args:
            player: The player assigned to the AI.
            max_speed: The maximum speed the AI can reach.
            count_paths_to_check: The number of paths used to avoid dead ends.
            depth: Depth pre-calculating actions.
            paths_tolerance: A tolerance, whereby more than just the best action is calculated. Actions which are
                worse, but within this tolerance, are also considered.
                depth: Number of player actions that are looked into the future.
            distance_to_check: Distance an enemy player is allowed to be at maximum distance, so that he is taken into
                account in the calculations.
        """
        PathfindingAI.__init__(self, player, max_speed, count_paths_to_check)
        SearchTreeAI.__init__(self, player, depth, max_speed, distance_to_check=distance_to_check)
        self.__paths_tolerance = paths_tolerance

    def get_information(self) -> str:
        """See base class."""
        return "max_speed=" + str(self._max_speed) \
               + ", paths_tolerance=" + str(self.__paths_tolerance) \
               + ", count_paths_to_check=" + str(self._get_count_paths_to_check()) \
               + ", depth=" + str(self._get_depth()) \
               + ", distance_to_check=" + str(self._get_distance_to_check())

    def create_next_action(self, game: Game, return_value: Value):
        """See base class."""
        self._turn_ctr += 1

        pathfinding_actions = self.create_next_actions_ranked(game)
        self.set_best_action(pathfinding_actions, [], return_value)
        search_tree_actions = self.create_all_next_surviving_actions(game)
        self.set_best_action(pathfinding_actions, search_tree_actions, return_value)

    def set_best_action(self, pathfinding_actions: List[Tuple[Action, int]], search_tree_actions: List[Action],
                        return_value: Value):
        """Saves the best action from the list of actions from PathfindingAI and SearchTreeAI.

        Args:
            pathfinding_actions: List of actions calculated by PathfindingAI.
            search_tree_actions: List of actions calculated by SearchTreeAI
            return_value: Object to save the result of the calculation.
        """
        best_action = self.get_best_action(pathfinding_actions, search_tree_actions)
        return_value.value = best_action.get_index() if best_action is not None else return_value.value

    def get_best_action(self, pathfinding_actions: List[Tuple[Action, int]],
                        search_tree_actions: List[Action]) -> Optional[Action]:
        """Calculates the best action from the list of actions from PathfindingAI and SearchTreeAI.

        Args:
            pathfinding_actions: List of actions calculated by PathfindingAI.
            search_tree_actions: List of actions calculated by SearchTreeAI

        Returns:
            Best action if there is any.
        """
        if search_tree_actions is None or len(search_tree_actions) == 0:
            if pathfinding_actions is not None and len(pathfinding_actions) > 0:
                return pathfinding_actions[0][0]
            return None
        elif pathfinding_actions is None or len(pathfinding_actions) == 0:
            return search_tree_actions[0]

        for (action, possible_paths) in pathfinding_actions:
            if action in search_tree_actions:
                if possible_paths == pathfinding_actions[0][1]:
                    return action  # best path and surviving guaranteed
                elif possible_paths >= pathfinding_actions[0][1] * self.__paths_tolerance:
                    return action  # good path and surviving guaranteed
                else:
                    break

        return pathfinding_actions[0][0]

Ancestors

Methods

def get_best_action(self, pathfinding_actions: List[Tuple[Action, int]], search_tree_actions: List[Action]) ‑> Optional[Action]

Calculates the best action from the list of actions from PathfindingAI and SearchTreeAI.

Args

pathfinding_actions: List of actions calculated by PathfindingAI.
search_tree_actions: List of actions calculated by SearchTreeAI

Returns

Best action if there is any.

Expand source code

def get_best_action(self, pathfinding_actions: List[Tuple[Action, int]],
                    search_tree_actions: List[Action]) -> Optional[Action]:
    """Calculates the best action from the list of actions from PathfindingAI and SearchTreeAI.

    Args:
        pathfinding_actions: List of actions calculated by PathfindingAI.
        search_tree_actions: List of actions calculated by SearchTreeAI

    Returns:
        Best action if there is any.
    """
    if search_tree_actions is None or len(search_tree_actions) == 0:
        if pathfinding_actions is not None and len(pathfinding_actions) > 0:
            return pathfinding_actions[0][0]
        return None
    elif pathfinding_actions is None or len(pathfinding_actions) == 0:
        return search_tree_actions[0]

    for (action, possible_paths) in pathfinding_actions:
        if action in search_tree_actions:
            if possible_paths == pathfinding_actions[0][1]:
                return action  # best path and surviving guaranteed
            elif possible_paths >= pathfinding_actions[0][1] * self.__paths_tolerance:
                return action  # good path and surviving guaranteed
            else:
                break

    return pathfinding_actions[0][0]

def set_best_action(self, pathfinding_actions: List[Tuple[Action, int]], search_tree_actions: List[Action], return_value: >)

Saves the best action from the list of actions from PathfindingAI and SearchTreeAI.

Args

pathfinding_actions: List of actions calculated by PathfindingAI.
search_tree_actions: List of actions calculated by SearchTreeAI
return_value: Object to save the result of the calculation.

Expand source code

def set_best_action(self, pathfinding_actions: List[Tuple[Action, int]], search_tree_actions: List[Action],
                    return_value: Value):
    """Saves the best action from the list of actions from PathfindingAI and SearchTreeAI.

    Args:
        pathfinding_actions: List of actions calculated by PathfindingAI.
        search_tree_actions: List of actions calculated by SearchTreeAI
        return_value: Object to save the result of the calculation.
    """
    best_action = self.get_best_action(pathfinding_actions, search_tree_actions)
    return_value.value = best_action.get_index() if best_action is not None else return_value.value

Classes

Attributes

Args

Ancestors

Methods

Args

Returns

Args

Inherited members