中山大学人工智能实验 (2019 秋)：16 个实验、4 个项目，包含搜索算法、CSP、Prolog、EM 算法、朴素贝叶斯、强化学习等

Haskell and Scala translations of Truth Maintenance Systems and other tools from Forbus and de Kleer's Building Problem Solvers.

A repository for AI Algorithms Lab Codes

Chineese-checker game by minmax algorithm and alpha beta algorithm

Projeto para descobrir o tipo de um Pokémon através de suas características básicas. Desenvolvido na disciplina da Inteligência Artificial.

Gomoku game engine with different AI search algorithms

AI Mini Project - Semester 20212, DSAI, HUST

Training snake using different Artificial Intelligence algorithms (A-STAR, BREADTH FIRST SEARCH, DEPTH FIRST SEARCH & ARTIFICIAL NEURAL NETWORKS). This game is made using pygame and python libraries.

This is a 2D car game for AI algorithms such as Reinforcement learning or Genetic algorithm.

I am trying to implement various AI algorithms on various environments (like OpenAI-gym) as I learned my toward the safe AI

Feedback prediction WinForm GUI. It is running AI algorithms based on the ML.NET model. It analyses feedback and gives output in a graph built on LiveCharts.

Implements AI Search algorithm to solve 9 puzzle problems or any n puzzles rather 😎

AIDesign Open Toolchain AI Research Application Collection

In this project I had to compare 3 algorithm's on the bases of there execution time. There are 3 AI algorithm's which were assign to me in these algorithm's two are uninform and one is inform. And the names of the algorithm's are: 1-DFS, 2-UCS, and A*

Searching Algorithm Example

Creates a Training environment to test different kinds of Artificial Intelligente algorithms to be used in the Fast and Curious game.

This program solves freecell solitaire puzzles using four algorithms: Depth first search, Breadth first search, Best first search and A*

Showcase of the contributions brought to the Halite-III joint homework

University of Tehran Artificial Intelligence Course Projects

This repository seeks to optimize bikes distribution of a public bicycle renting service across city stations using local search algorithms like Hill Climbing and Simulated Annealing, aiming to minimize costs and efficiently meet demand. It includes tools to visualize the distribution and showcases the utility of AI in urban logistics.