AI2 Project 1 — Bayes Net Inference (CS/AI Assignment)

This repository contains the code and supporting files for AI2 Project 1, which implements Bayesian network inference and reasoning as part of an introductory AI/ML course assignment.

The project includes Python code for representing Bayesian networks, performing exact and approximate inference, and applying probabilistic reasoning to example problems.

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📘 Project Overview

Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies via a directed acyclic graph (DAG). They are widely used for uncertainty modeling, prediction, and decision making under uncertainty.

In this project you will find implementations for:

• Constructing and representing Bayesian network structures
• Performing probabilistic inference (exact or approximate)
• Parsing test cases and running inference over them
• Integration with grading/autograder tools for automated evaluation

This codebase is adapted from the Berkeley AI (CS188) Bayes Net Inference Project structure.

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📁 Repository Structure

AI2_project1/
├── autograder.py
├── bayesAgents.py
├── bayesNet.py
├── bayesNets2TestClasses.py
├── factorOperations.py
├── game.py
├── ghostAgents.py
├── grading.py
├── graphicsDisplay.py
├── graphicsUtils.py
├── hunters.py
├── inference.py
├── keyboardAgents.py
├── layout.py
├── pacmanAgents.py
├── projectParams.py
├── submission_autograder.py
├── testClasses.py
├── testParser.py
├── textDisplay.py
├── util.py
├── VERSION
└── README.md

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🧠 What’s Included

Core Components

• bayesNet.py Defines the classes and structures for representing Bayesian networks.

• inference.py Contains inference algorithms (e.g., exact and approximate inference) for querying the network.

• factorOperations.py Implements operations on factors needed for probabilistic calculations (e.g., join, marginalize, normalize).

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🧪 Testing & Autograder

• autograder.py / submission_autograder.py Scripts to automatically run tests and verify correctness of your implementations.

• testClasses.py / testParser.py / bayesNets2TestClasses.py Test harness and test case definitions for validating functionality.

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▶️ How to Run

1. Make sure you have Python 3 installed.

2. To run the autograder and test your solutions:

   python3 autograder.py

   Or to run specific tests:

   python3 testParser.py

3. You can also import the modules in your own Python scripts and experiment with Bayesian networks:

   from bayesNet import BayesNet
   from inference import inferenceFunction

(Replace inferenceFunction with the relevant inference method you want to use.)

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🛠 Dependencies

This project uses standard Python libraries only — no external packages are required. It is intended to work with the default Python installation.

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📝 Notes

• This repository is structured for coursework and learning, not as a production library.
• Files like game.py, layout.py, and pacmanAgents.py are included to support any examples or test interactions with graphical/state environments.

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📄 License

The original project structure and code are adapted from educational materials. Please respect any original licensing terms if r