# UniformCost_AI_py
Uniform Cost Search Algorithm implemented in Python. This is a school project for Artificial Intelligence.
There are 2 versions available. Version "maynard_hw1_r1.py" is a NetworkX implementation that solves the problem with Dijkstra algorithm. Version "maynard_hw1_r5.py" implements the Uniform-Cost Search (UCS) algorithm.
The pseudo-code for the algorithm is as follows:
# UCS Pseudocode:
# Let fringe be a priority queue containing initial state
# Loop
# if fringe is empty
# return failure
# node = remove-first(fringe)
# if node is goal
# return path from initial state to node
# else
# generate all successors of node and put the newly generated
# nodes into fringe according to their cost values
#
# References:
# Korzhova, Lecture 5
# "Artificial Intelligence A Modern Approcah", Russell, Norvig
UniformCost_AI_py-master.zip （9个子文件） 
UniformCost_AI_py-master 
maynard_hw1_r5.py 5KB
input.txt 70B
Screen Shot graph.pdf 616KB Screen Shot result.pdf 353KB README.md 882B maynard_hw1_r1.py 3KB readme_maynard_hw1.txt 1KB maynard_UCS_python.pdf 463KB
maynard_HW1.docx 52KB资源评论
