Created a pacman simulation in Python, as a part of Berkeley's University Artificial Intelligence course. Project is divided into two parts. Firstly a basic agent and multiple search algorithms are implemented. In the second part, problem became more complex, while designing intelligent multiagents.
In this project, Pacman agent finds paths through his maze world, both to reach a particular location and to collect food efficiently. General search algorithms were built and applied them to Pacman scenarios. For more information, read the following analytical instructions.
- Depth First Search
- Breadth First Search
- Uniform Cost Search
- A* Search
In this project, agents for the classic version of Pacman were designed, including ghosts. Along the way, both minimax and expectimax search were implemented. I also experimented with evaluation function design. For more information, read the following analytical instructions.
- Reflex Agent
- Minimax
- Alpha-Beta Pruning
- Expectimax
- Evaluation Function