Add Missionaries and Cannibals Problem Solution with BFS and DFS Implementations

[UditNayak]

Issue: #500

Issue Name: Request to get Community Solution for Exercise 'ex_6' in '3-Solving-Problems-By-Searching'

Description:

This PR addresses the solution for Exercise 'ex_6' in the '3-Solving-Problems-By-Searching' section. The code provides a solution to the classic Missionaries and Cannibals problem using both Breadth-First Search (BFS) and Depth-First Search (DFS) strategies. The implementation involves creating a structure to represent the state of the problem and defining methods to handle state transitions and validity checks.

Changes Made:

1. Problem State Representation:

   • Introduced RiverSide class to represent the state of missionaries and cannibals on each side of the river.
   • Added methods to handle state transitions (__add__, __sub__), validity checks (isValid), and possible actions (action).

2. Custom Graph-Search BFS Implementation:

   • Implemented a custom BFS method in the Mc class to solve the problem.
   • Utilized the Queue module to manage the frontier in the BFS search.
   • Added a solve method to run the BFS and check for the solution.

3. DFS Integration:

   • Integrated DFS using the depth_first_graph_search method from the provided search module.
   • Added a solve2 method to run the DFS and check for the solution.

4. Action and Result Handling:

   • Defined actions method in the Mc class to generate possible actions from the current state.
   • Defined result method to compute the resulting state after applying an action.

5. Goal Testing:

   • Implemented goal_test method to check if the current state matches the goal state.

Testing:

• Provided a main function to test the problem and verify the solution using both BFS and DFS methods.

Notes:

• The custom BFS implementation was derived independently before discovering the general implementation available in the aima-python repository.
• The solution ensures that state transitions are valid and adheres to the problem constraints.