aistudent 1.0.2

A simple AI utilities package for search, CSP, and games.

AI Utilities Package

This package provides essential implementations of AI algorithms, making it easier for students to learn and experiment with search algorithms, constraint satisfaction problems (CSP), and game-playing strategies. It is based on the AIMA-Python project but has been modularized and simplified for educational use.

Features

• Search Algorithms: Implements uninformed (BFS, DFS, UCS) and informed (Greedy, A*) search algorithms.
• Constraint Satisfaction Problems (CSP): Includes backtracking search, forward-checking, and heuristics for CSPs.
• Game Playing: Implements Minimax and Alpha-Beta pruning for decision-making in two-player games.

Installation

To install this package, use:

pip install aistudent-1.0.0-non-any-py312.whl

Dependencies

• numpy
• networkx
• sortedcontainers
• scipy
• matplotlib

These dependencies will be installed automatically, but you can manually install them using:

pip install numpy networkx sortedcontainers scipy matplotlib

Usage

Once installed, you can import and use the package in your Python scripts.

Example 1: Using BFS from Search Module

from aiutils import search

problem = search.GraphProblem('A', 'B', some_graph)
solution = search.breadth_first_search(problem)
print(solution)

Example 2: Solving a CSP Problem

from aiutils import csp

variables = ['A', 'B', 'C']
domains = {'A': [1, 2], 'B': [1, 2], 'C': [1, 2]}
neighbors = {'A': ['B'], 'B': ['A', 'C'], 'C': ['B']}
problem = csp.CSP(variables, domains, neighbors)

solution = csp.backtracking_search(problem)
print(solution)
``
### Example 3: Minimax Algorithm for Game AI
```sh
from aiutils import game

game_state = game.TicTacToe()
move = game.minimax_decision(game_state, game_state.player)
print("Best Move:", move)

Credits & Contributions

This package is based on the AIMA-Python implementations by Stuart Russell & Peter Norvig, originally developed as part of Artificial Intelligence: A Modern Approach.

Original Implementation: AIMA-Python Contributors

Refactored & Simplified for Students: [Babar Ahmad]

Package Creation & Modularization: [Babar Ahmad]

This package is designed to simplify AI learning for students by providing easy-to-use, modular AI implementations.