Implementations of AI Search Techniques: Generate-And-Test and Hill Climbing

Overview

This repository contains Python implementations of two foundational search techniques in Artificial Intelligence:

1. Generate-And-Test Algorithm
2. Hill Climbing Algorithm

These algorithms are used to explore and solve optimization problems effectively by navigating a search space.

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Features

• Generate-And-Test:

  • Explores all possible solutions and tests them to find the correct or optimal one.

• Hill Climbing:

  • Iteratively improves the solution by moving to a better neighboring state until a local optimum is reached.

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Example Scenarios

• Generate-And-Test:

  • Finds the correct solution from a set of possibilities, such as solving a puzzle or identifying a valid combination.

• Hill Climbing:

  • Solves optimization problems like finding the shortest path, maximizing utility, or other similar scenarios.

Requirements

• Python 3.7 or later
• No additional libraries are required for basic functionality.

Notes

• The algorithms are implemented in Python for educational purposes.
• Hill Climbing may terminate at a local optimum depending on the problem space.