
AutoToS prompt:

This domain models the classic Sokoban game, where the robot needs to move around and push stones to specific locations while adhering to the constraints defined by the walls in the maze. 
The maze is defined by a grid of values 0,1, and 2, where 2 means it is a goal location for a stone, 1 means the cell is blocked, and either 0 or 2 means that the cell can be occupied. A cell is clear if it can be occupied, but is not occupied by either the player or any stone.

The actions are:

move: This action moves the player in a specified direction. To perform this action, the target location must be clear and adjacent to player's location. Performing this action will result in the player being at the target location and no longer at the starting location, meaning the starting location is now clear, while the target location is now not clear.

push: This action allows the player to push a stone in a specified direction. To perform this action, the player's location, the stone location, and the target location must line up in the same direction, and the target location must be clear. Performing the action results in player being at the stone's initial location and the stone is at the target location. Further, the player's previous location is clear, while the target location is not clear.

Assume that states are defined by a dictionary with keys at-player and at-stone. 
Here is an example of a state
{'at-player': (4, 4), 'at-stone': [(2, 2), (3, 3)]}
Here is an example of the grid:
[[1, 1, 1, 1, 0, 0, 0, 0], [1, 0, 0, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1, 1, 1], [1, 0, 0, 0, 1, 0, 0, 1], [1, 1, 0, 0, 0, 0, 0, 1], [0, 1, 1, 1, 2, 2, 0, 1], [0, 0, 0, 1, 0, 1, 0, 1], [0, 0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 1, 1, 1, 1, 1]]

Provide a Python implementation of successor states function, receiving a state and a grid and returning a list of immediate successor states.

Model response:
```python
def successor_states(state, grid):
    def is_clear(x, y):
        return 0 <= x < len(grid) and 0 <= y < len(grid[0]) and (grid[x][y] == 0 or grid[x][y] == 2)

    def move_player(x, y, new_x, new_y):
        return (new_x, new_y), state['at-stone']

    def push_stone(player_x, player_y, stone_x, stone_y, new_x, new_y):
        stones = state['at-stone'][:]
        stones.remove((stone_x, stone_y))
        stones.append((new_x, new_y))
        return (stone_x, stone_y), stones

    player_x, player_y = state['at-player']
    stone_positions = state['at-stone']
    successor_states = []

    directions = {
        "up": (-1, 0),
        "down": (1, 0),
        "left": (0, -1),
        "right": (0, 1)
    }

    for direction, (dx, dy) in directions.items():
        new_player_x = player_x + dx
        new_player_y = player_y + dy

        if is_clear(new_player_x, new_player_y):
            successor_states.append(move_player(player_x, player_y, new_player_x, new_player_y))
        
        for stone_x, stone_y in stone_positions:
            if (stone_x, stone_y) == (new_player_x, new_player_y):
                target_x = stone_x + dx
                target_y = stone_y + dy
                if is_clear(target_x, target_y):
                    successor_states.append(push_stone(player_x, player_y, stone_x, stone_y, target_x, target_y))

    return successor_states
```

AutoToS prompt:

Provide only the python code that tests whether a state is a goal state for a given goal configuration. Assume that the goal configuration is provided by the 2-dimensional array as before. The function should receive a state and the 2-dimensional array and return True if the state is a goal state and False otherwise.

Model response:
```python
def is_goal_state(state, goal_configuration):
    def is_goal_position(pos):
        return goal_configuration[pos[0]][pos[1]] == 2

    stone_positions = state['at-stone']
    
    return all(is_goal_position(stone) for stone in stone_positions)
```

System message:
Goal Soundness Test Passed
Soundness test
Successor Iteration 1

AutoToS prompt:
The following exception was produced when testing the successor function: 
  File "<string>", line 5, in is_goal_state

tuple indices must be integers or slices, not str. Please fix this exception in the successor function.
Model response:
```python
def successor_states(state, grid):
    def is_clear(x, y):
        return 0 <= x < len(grid) and 0 <= y < len(grid[0]) and (grid[x][y] == 0 or grid[x][y] == 2)

    def move_player(x, y, new_x, new_y):
        return {'at-player': (new_x, new_y), 'at-stone': state['at-stone']}

    def push_stone(player_x, player_y, stone_x, stone_y, new_x, new_y):
        stones = state['at-stone'][:]
        stones.remove((stone_x, stone_y))
        stones.append((new_x, new_y))
        return {'at-player': (stone_x, stone_y), 'at-stone': stones}

    player_x, player_y = state['at-player']
    stone_positions = state['at-stone']
    successor_states = []

    directions = {
        "up": (-1, 0),
        "down": (1, 0),
        "left": (0, -1),
        "right": (0, 1)
    }

    for direction, (dx, dy) in directions.items():
        new_player_x = player_x + dx
        new_player_y = player_y + dy

        if is_clear(new_player_x, new_player_y):
            successor_states.append(move_player(player_x, player_y, new_player_x, new_player_y))
        
        for stone_x, stone_y in stone_positions:
            if (stone_x, stone_y) == (new_player_x, new_player_y):
                target_x = stone_x + dx
                target_y = stone_y + dy
                if is_clear(target_x, target_y):
                    successor_states.append(push_stone(player_x, player_y, stone_x, stone_y, target_x, target_y))

    return successor_states
```

System message:
Successor States Soundness Test Passed
Completeness test
Successor Completeness Test Passed
Found solution of length 25, optimal: 69
Found solution of length 1, optimal: 35
Found solution of length 21, optimal: 95
Found solution of length 17, optimal: 92
Found solution of length 27, optimal: 49
Found solution of length 7, optimal: 429
Found solution of length 3, optimal: 51
Found solution of length 21, optimal: 83
Found solution of length 17, optimal: 35
Found solution of length 16, optimal: 76
Found solution of length 57, optimal: 107
Total success: 0, failure: 11, total expansions 364051, total generated 1109339
