yogabharathi3/19ai405expno10

ExpNo:10 Implementation of Classical Planning Algorithm

DEVELOPED BY: Yogabharathi S

REG NO: 212222230179

AIM:

To Implementation of Classical Planning Algorithm

Algorithm:

1. Define the initial state
2. Define the goal state
3. Define the actions
4. Find a plan to reach the goal state
5. Print the plan

Example - 1

initial_state = {'A': 'Table', 'B': 'Table'}
goal_state = {'A': 'B', 'B': 'Table'}

actions = {
    'move_A_to_B': {'precondition': {'A': 'Table', 'B': 'Table'}, 'effect': {'A': 'B'}},
    'move_B_to_Table': {'precondition': {'A': 'Table', 'B': 'B'}, 'effect': {'B': 'Table'}}
}

plan = find_plan(initial_state, goal_state, actions)
print(plan)

Output:

['move_A_to_B']

Example - 2

initial_state = {'A': 'Table', 'B': 'Table', 'C': 'Table'}
goal_state = {'A': 'B', 'B': 'C', 'C': 'Table'}

actions = {
    'move_A_to_B': {'precondition': {'A': 'Table', 'B': 'Table'}, 'effect': {'A': 'B'}},
    'move_B_to_C': {'precondition': {'A': 'B', 'B': 'Table', 'C': 'Table'}, 'effect': {'B': 'C'}},
    'move_C_to_Table': {'precondition': {'A': 'B', 'B': 'C', 'C': 'C'}, 'effect': {'C': 'Table'}}
}

plan = find_plan(initial_state, goal_state, actions)
print(plan)

Output:

['move_A_to_B', 'move_B_to_C']

Please Prepare Solution or Definition For the method find_plan(initial_state, goal_state, actions)

You Can use any of the searching Strategies for planning and executing a sequence of actions.
You can also look in to the Code given in the Repository.

PROGRAM:

def is_goal_state(current_state, goal_state):
    return current_state == goal_state

def apply_action(current_state, action_effect):
    new_state = current_state.copy()
    new_state.update(action_effect)
    return new_state

def find_plan(initial_state, goal_state, actions):
    queue = [(initial_state, [])]
    visited_states = set()

    while queue:
        current_state, partial_plan = queue.pop(0)

        if is_goal_state(current_state, goal_state):
            return partial_plan

        if tuple(current_state.items()) in visited_states:
            continue

        visited_states.add(tuple(current_state.items()))

        for action in actions:
            if is_applicable(current_state, actions[action]['precondition']):
                next_state = apply_action(current_state, actions[action]['effect'])
                queue.append((next_state, partial_plan + [action]))

    print("No plan exists.")
    return None
def is_applicable(current_state, precondition):
    return all(current_state.get(key) == value for key, value in precondition.items())
# Example usage
'''
initial_state = {'A': 'Table', 'B': 'Table'}
goal_state = {'A': 'B', 'B': 'Table'}

actions = {
    'move_A_to_B': {'precondition': {'A': 'Table', 'B': 'Table'}, 'effect': {'A': 'B'}},
    'move_B_to_Table': {'precondition': {'A': 'Table', 'B': 'B'}, 'effect': {'B': 'Table'}}
}

plan = find_plan(initial_state, goal_state, actions)
print(plan)
'''
initial_state = {'A': 'Table', 'B': 'Table', 'C': 'Table'}
goal_state = {'A': 'B', 'B': 'C', 'C': 'Table'}

actions = {
    'move_A_to_B': {'precondition': {'A': 'Table', 'B': 'Table'}, 'effect': {'A': 'B'}},
    'move_B_to_C': {'precondition': {'A': 'B', 'B': 'Table', 'C': 'Table'}, 'effect': {'B': 'C'}},
    'move_C_to_Table': {'precondition': {'A': 'B', 'B': 'C', 'C': 'C'}, 'effect': {'C': 'Table'}}
}

plan = find_plan(initial_state, goal_state, actions)
print(plan)
'''
initial_state = {'A': 'Table', 'B': 'Table'}
goal_state = {'A': 'Table', 'B': 'Table'}

actions = {
    'move_A_to_B': {'precondition': {'A': 'Table', 'B': 'Table'}, 'effect': {'A': 'B'}}
}

plan = find_plan(initial_state, goal_state, actions)
print(plan)
'''

RESULT:

Therefore,Implementation of Classical Planning Algorithm is implemetated successfully.