aima-toolkit 2.1.2

I made this package to help me on my learning journey through the AIMA book and contains all algorithms I build whilst learning through the book

🧠 AIMA Toolkit — Artificial Intelligence Algorithms in Python

The AIMA Toolkit is a personal project that re-implements and extends the algorithms described in Artificial Intelligence: A Modern Approach (4th Edition) by Russell & Norvig — written entirely from scratch in Python.

It serves as a modular foundation for AI experimentation, algorithm benchmarking, and educational purposes.
The package is now published on PyPI and will continue to expand over time with detailed documentation and advanced examples.

📘 Note: As of now, there is no documentation available since this is a solo projects. But I will make one as I continue to make progress in the development of this repository

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📦 Installation

Available on PyPI:

pip install aima-toolkit

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🧩 Current Capabilities

The AIMA Toolkit currently supports a growing suite of intelligent agents, search algorithms, and problem-solving paradigms.

🤖 Search Agents

Supports the creation of both simple and advanced agents, including:

• Sensorless Search Agents
• Partially Observable Agents
• Fully Observable Agents
• Deterministic and Non-Deterministic Agents
• Online and Offline Agents
• Informed & Uninformed Search Variants

Each agent type can be combined with search strategies such as A*, BFS, DFS, Uniform-Cost, Greedy Best-First, Iterative Deepening, and much more.

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🔢 Constraint Satisfaction Problems (CSP)

The toolkit includes a fully modular CSP framework, allowing easy definition and solving of different constraint types:

• Variable and domain registration
• AllDiff, Binary, and Custom constraints

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🧮 Local Search

Implements a wide range of local search algorithms, including:

• Hill Climbing (with and without random restarts)
• Simulated Annealing
• Stochastic Beam Search
• Genetic Algorithms (planned extension)

These algorithms are designed for optimization, learning, and CSP problems where global solutions are infeasible to compute directly.

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🧠 Demonstration Projects

To showcase the toolkit’s power, I’ve developed two full-featured projects based entirely on this framework:

Project	Description	Repository
🧩 N-Puzzle Solver	A Disjoint Pattern Database (PDB) enhanced N-Puzzle solver built on top of the AIMA Toolkit’s A* infrastructure.	🔗 View on GitHub
🔢 Sudoku All-in-One	A comprehensive Sudoku CSP solver supporting Classic, Diagonal, Kropki, and German Whispers variants — using advanced constraint propagation from AIMA Toolkit.	🔗 View on GitHub

These serve as live examples of the toolkit’s modularity and its use in building powerful AI applications.

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🧬 Project Philosophy

This project aims to create a comprehensive, modular, and educational AI framework that remains close to theoretical foundations while being practical for modern Python environments.

Core principles:

• 🧩 Readable and modular implementations
• 🧠 Faithful to algorithmic definitions from AIMA (4th Ed.)
• ⚙️ Designed for learning, experimentation, and scalability

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💬 Author

Emre Arapčić-Uevak
📍 Bosnia & Herzegovina
🎓 Computer Science & Engineering — International University of Sarajevo
🔗 GitHub Profile

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🪶 License

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