krr-research-snippets

A curated collection of Python‑based Knowledge Representation & Reasoning (KRR) exercises, including an interactive Jupyter notebook and two standalone logic mini‑projects.

Table of Contents

• About
• Repository Structure
• Prerequisites
• Installation
• Usage
  • Lab 7 Notebook
  • Mini Projects
• Author’s Expertise
• Concepts and Ideas
• Contributing
• License

About

This repository showcases core AI techniques for modeling and automated reasoning, demonstrating how real‑world problems and classic puzzles can be encoded, solved, and explored in Python.

Repository Structure

krr-research-snippets/ ├── Labs/ │ └── lab 7.ipynb # Jupyter notebook: employee salary calculation exercise ├── mini-projects/ │ ├── Mini Project 1.py # Logic puzzle solver (truth-tellers & liars) │ └── Mini_Project 2.py # English ↔ predicate-logic translator ├── LICENSE # MIT License └── README.md # This file

Prerequisites

• Python 3.7 or higher
  
• (Optional) Jupyter Notebook for interactive execution
  

Installation

1. Clone the repository:
   bash git clone https://github.com/amr-yasser226/krr-research-snippets.git cd krr-research-snippets
   
2. (Optional) Install Jupyter:
   bash pip install jupyter

Usage

Lab 7 Notebook

Launch the notebook to explore conditional business‑rule encoding and salary computations: bash jupyter notebook Labs/lab\ 7.ipynb

Mini Projects

• Mini Project 1: Solve classic “truth‑tellers vs. liars” puzzles by brute‑force enumeration
  bash python "mini-projects/Mini Project 1.py"
• Mini Project 2: Translate simple English quantifier sentences to first‑order logic and back
  bash python "mini-projects/Mini_Project 2.py"

Author’s Expertise

• Python Programming: clear, idiomatic implementations
  
• Interactive Data Science: Jupyter notebooks for narrative‑driven code
  
• Logic & Automated Reasoning: puzzle solving via search and formal logic
  

Concepts and Ideas

• Knowledge Representation & Reasoning: structuring information so machines can infer and decide
  
• Automated Search: exhaustive truth‑value enumeration for Knights & Knaves puzzles
  
• Predicate Logic Translation: mapping between natural language and formal quantifier syntax
  

Contributing

Contributions, new snippets, and improvements are welcome! Please open an issue or submit a pull request.

License

This project is released under the MIT License. See the LICENSE file for details.