Computational-Intelligence

Computational Intelligence course at Politecnico di Torino. For detailed information about the repository, please refer to the log.

Topics

• Search Algorithms
  • Depth-First Search (DFS)
  • Breadth-First Search (BFS)
  • Uniform-Cost Search (UCS)
  • A*
  • Greedy Best-First Search
  • Hill Climbing
  • Simulated Annealing
  • Genetic Algorithms
  • Evolutionary Algorithms / Strategies
• Reinforcement Learning
  • Q-Learning
  • Deep Q-Learning
• Adversarial Search
  • Minimax
  • Alpha-Beta Pruning

Structure

• challenges: contains the challenges proposed during the course.
  • halloween: set-covering problem solved with optimization algorithms.
  • min_sum: finds the minimum amount of numbers in a set that sum to a given number using DFS and BFS.
• extras: contains extra material.
  • aux: auxiliary files.
  • env: set of instructions to create an environment.
  • lib: library for search algorithms.
  • previous_work: previous work done on the computational intelligence field.
• labs: laboratory sessions.
  • lab1: Set-Covering problem with search algorithms (DFS, BFS, UCS, A*)
  • lab2: Nim strategy with ES.
  • lab9: Evolutionary Algorithms.
  • lab10: Tic-Tac-Toe with Minimax and Reinforcement Learning.
• quixo: Quixo game with Minimax and Reinforcement Learning.