AI Pacman - Multi-Agent Search
Advanced AI for Pacman featuring DFS, BFS, A*, and minimax with alpha-beta pruning.
- Role: Developer
- Timeline: October 2020
- Team: AI Course Assignment
- Technologies: Python, AI/ML, Algorithms, Game AI
- Link: https://github.com/Laxman824/Projects-Assignments/tree/main/AI-Pacman%20heuristics
Problem
UC Berkeley's AI course project: implement various search algorithms and adversarial agents for Pacman, demonstrating understanding of AI fundamentals.
Solution
Implemented complete suite: uninformed search (DFS, BFS), informed search (A*, Greedy), adversarial search (Minimax, Alpha-Beta, Expectimax), and custom evaluation functions.
Impact
- Full score on all autograder tests
- Optimal pathfinding implementation
- Multi-ghost adversarial handling
- Top performance in class
Key features
- Depth-First Search (DFS)
- Breadth-First Search (BFS)
- Uniform Cost Search
- A* with heuristics
- Minimax adversarial search
- Alpha-Beta pruning optimization
- Expectimax for stochastic agents
- Custom evaluation functions
Tech stack
- Language: Python
- Concepts: Graph Search, Game Theory, Heuristics
- Framework: UC Berkeley Pacman
What K Laxman learned
- Search algorithm implementations
- Game tree evaluation
- Heuristic design for A*
- Alpha-beta pruning optimization
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