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AI Problem Solvers (CS131 Tufts)

This repository contains a collection of projects completed as part of the CS131 course at Tufts University. The focus of these projects is on solving well-known problems using artificial intelligence concepts, with all implementations built from scratch. The goal is to explore AI concepts through hands-on problem-solving without relying on external libraries.

Projects

  • Gardens of Heaven - Artificial Neural Networks
  • Radar Trace Classifier - Naive Bayes Classification
  • Sudoku Solver - Constraint Satisfaction Problems
  • The Knapsack Problem - Genetic Algorithms
  • The Pancake Problem - Informed Search
  • Vacuum Cleaning Robot - Behavior Trees

Each project addresses a famous problem, demonstrating the application of various AI methodologies, and is intended to showcase fundamental AI techniques in a clear and accessible way.

Features:

  • Custom AI implementations: All algorithms are implemented from scratch.
  • Focus on classical AI problems: Tackling widely known challenges with AI approaches.
  • Educational resource: A hands-on collection of AI problem-solving techniques.

Feel free to explore the projects and dive into the AI concepts behind each one.

About

This repository features AI solutions to classic problems, built from scratch using techniques like neural networks, genetic algorithms, and behavior trees. Projects tackle challenges such as Sudoku, the Knapsack problem, and more, providing hands-on applications of core AI concepts.

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