CSE 571 Artificial Intelligence
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Updated
Jan 3, 2018 - Python
CSE 571 Artificial Intelligence
Visualization for multiple searching algorithms.
Implement Algorithms For Graph Search (like A*) & Local Search (like hill climbing algorithms) & Genetics
A fine-tuned visual implementation of Informed and Uninformed Search Algorithms such as Breadth First Search, Depth First Search, Uniform Cost Search, A* Search, Greedy First Search
Uniform Cost Search implementation for Artificial Intelligence course
Programs developed for CSCI561 Foundations of Artificial Intelligence course
Desktop app for visualizing graph search algorithms
All Artificial Intelligence Search algorithms. Depth first search, Breadth first search, uniform cost search, Greedy search, A star search, Minimax and Alpha beta pruning. Implemented in Python 3.
Planning Project Implementation for the Udacity Artificial Intelligence Nanodegree Program
The algorithm determines the least cost path from the start location to goal location
Program that searches for the shortest route using the 'Uniform Cost Search' algorithm by consulting a map of the province of Santo Domingo extracted from OpenStreetMap.
Solves Sokoban Puzzles using A* search, UCS algorithms and heuristic functions
Calculating the shortest path between two nodes with the Uniform Cost Search algorithm.
Robot that cleans room from dirts. Finds the optimum path eventually. Same algorithms are applied as in finding path to escape a maze.
Implementations of artificial intelligence agents that plays Pac-Man
A visualisation tool for various pathfinding algorithms.
Implementation of UCS algorithm in Python
👻 🎮 This is my implementation in the famous Berkeley pacman artificial intelligence project: http://ai.berkeley.edu/project_overview.html.
This program solves a 2D maze with the help of several search algorithms like BFS, DFS, A* (A-Star) etc.
UCS(Uniform Cost Search for Directed and Undirected Graph Using Vertice List and Matrix Representation
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