Berkeley implementation of Pacman AI adapted by Nelson Ponzoni only for teaching propourses
Files you'll edit: search.py: Where all of your search algorithms will reside. searchAgents.py: Where all of your search-based agents will reside. Files you might want to look at: pacman.py: The main file that runs Pacman games. This file describes a Pacman GameState type, which you use in this project. game.py: The logic behind how the Pacman world works. This file describes several supporting types like AgentState, Agent, Direction, and Grid. util.py: Useful data structures for implementing search algorithms.
The Pac-Man projects were developed for UC Berkeley's introductory artificial intelligence course, CS 188. They apply an array of AI techniques to playing Pac-Man. However, these projects don't focus on building AI for video games. Instead, they teach foundational AI concepts, such as informed state-space search, probabilistic inference, and reinforcement learning. These concepts underly real-world application areas such as natural language processing, computer vision, and robotics.
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P1 - Search: implement depth-first, breadth-first, uniform cost, and A* search algorithms. These algorithms are used to solve navigation and traveling salesman problems in the Pacman world.
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P2 - Multi-Agent Search: implement multiagent minimax and expectimax algorithms, as well as designing evaluation functions.
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P3 - Reinforcement Learning: implement model-based and model-free reinforcement learning algorithms, applied to the AIMA textbook's Gridworld, Pacman, and a simulated crawling robot.
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P4 - Ghostbusters: implement exact inference using the forward algorithm and approximate inference via particle filters.
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P5 - Classification: implement standard machine learning classification algorithms using Naive Bayes, Perceptron, and MIRA models to classify digits then extend this by implementing a behavioral cloning Pacman agent.
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P6 - Contest: Pacman Capture the Flag: create strategies for a team of two agents to play a multi-player capture-the-flag variant of Pacman.
P1 - Search
All those colored walls, mazes give Pacman the blues, so teach him to search. Implement depth-first, breadth-first, uniform cost, and A* search algorithms. These algorithms are used to solve navigation and traveling salesman problems in the Pacman world.
search.py - the search algorithms
searchAgents.py - the search-based agents
pacman.py - the main file that runs Pacman games
game.py - the logic behind how the Pacman world works
util.py - useful data structures for implementing search algorithms
- Q1: Depth First Search (
search.py-depthFirstSearch) - Q2: Breadth First Search (
search.py-breadthFirstSearch) - Q3: Uniform Cost Search (
search.py-uniformCostSearch) - Q4: A* Search (
search.py-aStarSearch) - Q5: Corners Problem: Representation (
searchAgents.py-CornersProblem) - Q6: Corners Problem: Heuristic (
searchAgents.py-cornersHeuristic) - Q7: Eating All The Dots: Heuristic (
searchAgents.py-FoodSearchProblem) - Q8: Suboptimal Search (
searchAgents.py-findPathToClosestDot)