Sztuczna Inteligencja i Systemy Ekspertowe / Artificial Intelligence and Expert Systems ✅

Author: Student project repository (Semestr 4)
Language: Python 3.x

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📌 Project Overview

This repository contains coursework for the subject Sztuczna Inteligencja i Systemy Ekspertowe (Artificial Intelligence and Expert Systems). It includes three main assignments (Zadanie1, Zadanie2, Zadanie3) that demonstrate search algorithms, a neural network (MLP) implementation, and a simple ML analysis example.

🗂️ Project Structure

• Zadanie1/ — Fifteen Puzzle solver (BFS, DFS, A*) with research scripts and plotting utilities.
  • main.py, solver.py, chartGenerator.py, filesGenerator.py, plots/, resources/
• Zadanie2/ — Multilayer Perceptron (MLP) implementation and tools for training/testing (Iris dataset example).
  • mlp.py, layer.py, encoder.py, dataLoader.py, test.py, UI.py, irisset.csv
• Zadanie3/ — Data analysis and regression example (Decision Tree Regressor with learning curves).
  • app.py, data.csv

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🚀 Quick Start

Prerequisites:

• Python 3.8+
• Recommended packages: numpy, pandas, scikit-learn, matplotlib

Install dependencies (example):

pip install numpy pandas scikit-learn matplotlib

Run examples:

• Zadanie1 (Fifteen Puzzle):

  python Zadanie1/main.py bfs RDUL path/to/input.txt path/to/solution.txt path/to/stats.txt

  See Zadanie1/README.md for detailed input/output format and research instructions.

• Zadanie2 (MLP / Iris example):

  • Train or test a network using provided scripts (see Zadanie2/ files). For example, load a saved network and run tests from Zadanie2/test.py.

• Zadanie3 (Regression demo):

  python Zadanie3/app.py

  This will run a Decision Tree regressor on data.csv and show learning curves and trees.

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🧪 Notes & Usage Tips

• Input/output formats and usage examples for the Fifteen Puzzle are documented in Zadanie1/README.md.
• The MLP implementation (Zadanie2/mlp.py) includes logging and save/load options; inspect mlp.py and test.py for usage patterns.

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📖 How to Contribute

• Add tests or small improvements via pull requests.
• Keep changes well-documented and include example invocations for any new scripts.

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This README was generated to provide a concise overview and usage guide for the course project.