GitHub - mikexie360/aima-python: Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence

mikexie360 / aima-python Public

forked from aimacode/aima-python

Notifications You must be signed in to change notification settings
Fork 0
Star 0

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

MIT license

0 stars 3.6k forks Branches Tags Activity

Star

Notifications

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 1,199 Commits
aima-data @ f6cbea6		aima-data @ f6cbea6
gui		gui
images		images
js		js
notebooks		notebooks
tests		tests
.coveragerc		.coveragerc
.flake8		.flake8
.gitignore		.gitignore
.gitmodules		.gitmodules
.travis.yml		.travis.yml
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.txt		README.txt
SUBMODULE.md		SUBMODULE.md
agents.ipynb		agents.ipynb
agents.py		agents.py
agents4e.py		agents4e.py
arc_consistency_heuristics.ipynb		arc_consistency_heuristics.ipynb
classical_planning_approaches.ipynb		classical_planning_approaches.ipynb
csp.ipynb		csp.ipynb
csp.py		csp.py
deep_learning4e.py		deep_learning4e.py
dontRead.md		dontRead.md
games.ipynb		games.ipynb
games.py		games.py
games4e.ipynb		games4e.ipynb
games4e.py		games4e.py
improving_sat_algorithms.ipynb		improving_sat_algorithms.ipynb
index.ipynb		index.ipynb
intro.ipynb		intro.ipynb
ipyviews.py		ipyviews.py
knowledge.py		knowledge.py
knowledge_FOIL.ipynb		knowledge_FOIL.ipynb
knowledge_current_best.ipynb		knowledge_current_best.ipynb
knowledge_version_space.ipynb		knowledge_version_space.ipynb
learning.ipynb		learning.ipynb
learning.py		learning.py
learning4e.py		learning4e.py
learning_apps.ipynb		learning_apps.ipynb
logic.ipynb		logic.ipynb
logic.py		logic.py
logic4e.py		logic4e.py
making_simple_decision4e.py		making_simple_decision4e.py
mdp.ipynb		mdp.ipynb
mdp.py		mdp.py
mdp4e.py		mdp4e.py
mdp_apps.ipynb		mdp_apps.ipynb
neural_nets.ipynb		neural_nets.ipynb
nlp.ipynb		nlp.ipynb
nlp.py		nlp.py
nlp4e.py		nlp4e.py
nlp_apps.ipynb		nlp_apps.ipynb
notebook.py		notebook.py
notebook4e.py		notebook4e.py
obsolete_search4e.ipynb		obsolete_search4e.ipynb
p1.py		p1.py
p2.py		p2.py
p3.py		p3.py
p4.py		p4.py
perception4e.py		perception4e.py
planning.ipynb		planning.ipynb
planning.py		planning.py
planning_angelic_search.ipynb		planning_angelic_search.ipynb
planning_graphPlan.ipynb		planning_graphPlan.ipynb
planning_hierarchical_search.ipynb		planning_hierarchical_search.ipynb
planning_partial_order_planner.ipynb		planning_partial_order_planner.ipynb
planning_total_order_planner.ipynb		planning_total_order_planner.ipynb
probabilistic_learning.py		probabilistic_learning.py
probability.ipynb		probability.ipynb
probability.py		probability.py
probability4e.ipynb		probability4e.ipynb
probability4e.py		probability4e.py
pytest.ini		pytest.ini
reinforcement_learning.ipynb		reinforcement_learning.ipynb
reinforcement_learning.py		reinforcement_learning.py
reinforcement_learning4e.py		reinforcement_learning4e.py
requirements.txt		requirements.txt
search.ipynb		search.ipynb
search.py		search.py
search4e.ipynb		search4e.ipynb
text.ipynb		text.ipynb
text.py		text.py
utils.py		utils.py
utils4e.py		utils4e.py
vacuum_world.ipynb		vacuum_world.ipynb
viterbi_algorithm.ipynb		viterbi_algorithm.ipynb

Repository files navigation

Mike Xie
mxx170002@utdallas.edu
AI CS6364.001
HW1
This is my submission for Homework 1

This README.txt file will
1. list all commands which will compile the code on csgrads1.utdallas.edu
2. a list of commands which will run the code on csgrads1.utdallas.edu

----------------------------------------------------------------------
First download the elearning submission with the aima code and HW code
Send the file to csgrads1.utdallas.edu, through some FTP or ssh

-------------------------------------------------------------
cd into the aima code and  install numpy in your terminal
    run the following command in the aima directory
    pip3 install numpy --user

------------------------------------------------
p1.py is for kitty world with ability to control kitty
    does not solve any particular problem but is for problem 1
p2.py is for missionary and cannibal problem with various search aglorithms
    solves Problem 2.1 and 2.2
p3.py is for roadmap problem with RBFS and Astar
    Solves Problem 3.1 , 3.2 , 3.3 and 3.4
p4.py is for extra credit in problem 3
    Solves Problem 3 extra-credit

--------------------------------------------------
All python files are using python 3.
to run p1.py run
    python3 p1.py
you can then control kitty until kitty runs out of battery or you quit

to run p2.py run
    python3 p2.py
you will then see the solution path for the missionary problem

to run p3.py run
    python3 p3.py
you will then see the solution path for roadproblem with RBFS and Astar

to run p4.py run
    python3 p4.py
You will then see that the hueristic is inconsistent
------------------------------------------------------
All python runs on AIMA code, but some modifcations may have been made to into
p1.py kitty program may not have been needed.

For p3.py , I am assuming the direct flight distance is a good heuristic.

p4.py shows that flight distance is actually a pretty bad heuristic for road path.