prnvpwr2612 / AI_Codes

AI Algorithms and Problem Solving

Part A: Problem Analysis and Solving Techniques

This section explores various AI problem-solving approaches and techniques.

• PEAS Description
  Explore direct questions related to Problem, Environment, Actions, and Strategies (PEAS) or problem formulation in AI.

• Problem Formulation

• Forward Backward Chaining and Resolution
  Dive into the concepts of logical reasoning using forward/backward chaining and resolution methods.

Part B: Implemented Search Algorithms and Optimization Techniques

Explore a collection of AI algorithms and techniques implemented in this repository.

1. Breadth-First Search (BFS) / Depth-First Search (DFS) / Greedy Search
   Implementations of classic search algorithms for problem-solving.

2. Hill Climbing Search
   Implementation of the hill climbing algorithm for optimization problems.

3. A Star Algorithm
   Use the A* algorithm to efficiently find paths to goal states.

4. Minimax Algorithm
   Implementation of the minimax algorithm for decision-making in two-player games.

5. Ant Colony Optimization for Traveling Salesman Problem (TSP)
   Solve the TSP using ant colony optimization, a metaheuristic approach.

6. Genetic Algorithm for Optimization
   Utilize genetic algorithms to solve optimization problems by mimicking natural selection and genetic recombination.

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Feel free to explore the source code and documentation for detailed explanations and implementations of each algorithm.

Contributing

Contributions are welcome! If you'd like to add more algorithms, improve existing ones, or fix issues, feel free to fork this repository and submit a pull request.