inductive-logic-programming 1.1.0

Learning interpretable logical rules from examples combining symbolic reasoning with machine learning

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Inductive Logic Programming

🔍 Learning from examples

Muggleton, S. (1991) - "Inductive logic programming"

📦 Installation

pip install inductive-logic-programming

🚀 Quick Start

import inductive_logic_programming

# Create training examples
examples = [
    inductive_logic_programming.Example("parent(tom, bob)", True),
    inductive_logic_programming.Example("parent(bob, ann)", True), 
    inductive_logic_programming.Example("parent(tom, liz)", True),
    inductive_logic_programming.Example("parent(bob, pat)", True),
    inductive_logic_programming.Example("parent(pat, jim)", True),
    inductive_logic_programming.Example("grandparent(tom, ann)", True),
    inductive_logic_programming.Example("grandparent(tom, pat)", True),
]

# Use FOIL learner to discover rules
foil = inductive_logic_programming.FOILLearner()
learned_rules = foil.learn(examples)

print("✅ Learned rules:")
for rule in learned_rules:
    print(f"   {rule}")

# Alternative: Use Progol system
progol = inductive_logic_programming.ProgolSystem()
progol.set_examples(examples)
hypothesis = progol.learn()
print(f"✅ Progol hypothesis: {hypothesis}")

🎓 About the Implementation

Implemented by Benedict Chen - bringing foundational AI research to modern Python.

📧 Contact: [e-mail]

📖 Citation

If you use this implementation in your research, please cite the original paper:

Muggleton, S. (1991) - "Inductive logic programming"

📜 License

Custom Non-Commercial License with Donation Requirements - See LICENSE file for details.

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💰 Support This Work - Donation Appreciated!

This implementation represents hundreds of hours of research and development. If you find it valuable, please consider donating:

💳 DONATE VIA PAYPAL - CLICK HERE

Your support helps maintain and expand these research implementations! 🙏