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Intro
"Implemented various AI algorithms to optimize Pacman’s pathfinding, decision-making, ability to eat all dots, handle enemies, using Python to enhance efficiency and complexity management.
Tools
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Pathfinding Algorithms
Depth First Search (DFS): Explored deep paths first to find a solution.
Breadth First Search (BFS): Explored all nodes at the present depth level before moving on.
Uniform Cost Search (UCS): Considered the lowest cumulative cost paths.
A Search*: Used heuristics to improve search efficiency.
Implementation: Analyzed source files, used algorithm prompts, and pseudo-code references for smooth development.
Heuristic Problem-Solving
Corners Problem Heuristic: Ensured admissibility and consistency using Manhattan and maze distances.
Eating All Dots Heuristic: Optimized for the shortest path to consume all dots.
Reflex Agent: Immediate response based on current state.
Minimax: Decision-making algorithm for optimizing outcomes against an adversary.
AI Agents
Alpha-Beta Pruning: Improved Minimax by eliminating suboptimal branches.
Expectimax: Incorporated randomness for decision-making in uncertain environments.
Reinforcement Learning
Value Iteration: Solved known MDPs to find optimal policies.
Q-Learning: Learned optimal policies through trial and error in dynamic environments.
Epsilon-Greedy Strategy: Balanced exploration and exploitation for better learning outcomes.
Approximate Q-Learning: Used feature-based representation for learning in complex states.
Perceptron Implementation
Perceptron Model: Implemented basic linear classification using dot product and weight updates.
Training and Prediction: Iteratively trained the model for convergence and accurate predictions.
Build Approach
I began by analyzing source files and following Python tutorials to implement basic algorithms. For pathfinding, I used DFS, BFS, UCS, and A search algorithms in search.py. I then developed heuristics for specific problems like the Corners Problem and Eating All Dots. For AI agent design, I implemented algorithms in multiagents.py, focusing on Minimax, Alpha-Beta Pruning, and Expectimax. Reinforcement learning techniques, including value iteration and Q-learning, were applied to enable adaptive learning.
Results
The implemented algorithms significantly improved Pacman’s efficiency in pathfinding and decision-making. The AI agents demonstrated intelligent behavior in handling complex scenarios, and the reinforcement learning algorithms enabled Pacman to adapt and learn optimal strategies.
For more details on my work and other projects, please checkout my portfolio website.
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