def a_star(goal, frontier, env):
visited_states = []
actual_node = frontier.pop(0)
while env.matrix[actual_node.state.row][
actual_node.state.col].type != goal.type:
children = successor_function(actual_node, env.matrix)
for c in children:
if c.state not in visited_states:
frontier.append(c)
c.set_hValue(get_manhattan_distance(c, env.food_position))
if env.matrix[c.state.row][
c.state.col].type == CellTypes.TYPE_EMPTY:
env.matrix[c.state.row][c.state.col].set_expanded()
if env.matrix[actual_node.state.row][
actual_node.state.col].type == CellTypes.TYPE_EMPTY:
env.matrix[actual_node.state.row][
actual_node.state.col].set_visited()
visited_states.append(actual_node.state)
frontier.sort(key=lambda node: (node.hValue + node.gValue))
actual_node = frontier.pop(0)
return actual_node
def dfs(goal, frontier, space):
visited_states = []
actual_node = frontier.pop(0)
while space[actual_node.state.row][
actual_node.state.col].type != goal.type:
children = successor_function(actual_node, space)
for c in children:
if c.state not in visited_states:
frontier.insert(0, c)
if space[c.state.row][
c.state.col].type == CellTypes.TYPE_EMPTY:
space[c.state.row][c.state.col].set_expanded()
if space[actual_node.state.row][
actual_node.state.col].type == CellTypes.TYPE_EMPTY:
space[actual_node.state.row][
actual_node.state.col].set_visited()
visited_states.append(actual_node.state)
actual_node = frontier.pop(0)
return actual_node