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:

   git clone https://github.com/amr-yasser226/krr-research-snippets.git
   cd krr-research-snippets

2. (Optional) Install Jupyter:

   pip install jupyter

Usage

Lab 7 Notebook

Launch the notebook to explore conditional business‑rule encoding and salary computations:

jupyter notebook Labs/lab\ 7.ipynb

Mini Projects

• Mini Project 1: Solve classic “truth‑tellers vs. liars” puzzles by brute‑force enumeration

  python "mini-projects/Mini Project 1.py"

• Mini Project 2: Translate simple English quantifier sentences to first‑order logic and back

  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.