This repository is accomplished and will not be updated regularly, please issue if necessary.

@ 2019/12/28

Preview：

• Introduction
• Uninformed-Search
• Heuristic-Search
• Gametree-Search
• CSP
• KRR
• Planning
• Uncertainty
• Machine-Learning-1
• Machine-Learning-2
• Machine-Learning-3
• Machine-Learning-4
• Machine-Learning-5

Assignments had been removed for copyright issue.

Experiments would be organized as：

• Exx_17341137
  • Result: Store the results of the experiment.
  • Sourcecode: Store the source codes for the experiment.
  • Report
• Exx_date_name.zip
  • The experiment document.

Index

• Exp1

  Pacman, to find the shortest path towards the goal with DFS/BFS.

• Exp2

  15Puzzle problem, solved with ida* algorithm.

• Exp3

  Othello, AKA Reversi, with minmax tree(Negamax Tree) and alpha-beta pruning algorithm.

• Exp4

  Futoshiki, a SUDOKU-like puzzle game, with Forward Looking algorithm, using MRV(Most Resticted Values) Heuristics.

• Exp5

  Tutorial to Prolog, Family Tree.

• Exp6

  Use Prolog to solve simple FOL(first order logic) problem.

• Exp7

  Use PDDL to solve planning problem with STRIPS - BlocksWorld & 8-puzzle.

• Exp8

  Use PDDL to solve planning problem with STRIPS - BoxMan.

• Exp9

  Build a BN(Bayes Network) using pomegranate(an module in python).

• Exp10

  Implement VE(Variable Elimination) algorithm by hand, in python.

• Exp11

  Build a Decision Tree to solve classification problems, as a tutorial to ml.

• Exp12

  Use Naive Bayes algorithm to solve classification problems in Exp11.

• Exp13

  Use GMM(Gaussian Multivariate Model) with EM algorithm to solve clustering problem.

• Exp14

  Use Backward Propagation(BP) Algorithm to build a 3-layer NN with 1 hidden layer, to predict Horse-colic dataset.

• Exp15

  Implement a reinforcement learning tutorial and flappy bird model using Q-learning strategy.

• Exp16

  Implement a CNN with provided modules and utilities.

Experiments are all listed above, have fun here.

Projects would be organized as：

• Pxx_17341137
  • Result: Store the results of the experiment.
  • Sourcecode: Store the source codes for the experiment.
  • Report
• Pxx_name.zip
  • The project document.

Index

• Proj1

  A implementation of the Pacman, using minimax and alpha-beta pruning, from UC Berkeley CS188.

• Proj2

  • Futoshiki Solver with GAC algorithm and MRV, COMPARE with Exp4.
  • Use Prolog to solve the blocksworld problem.

• Proj3

  • Use Pddl to solve 2x2 Rubik’s Cube problem.
  • Implement VE(Variable Elimination) algorithm in BN(Bayes Network).

• Proj4

  A implementation of Reinforcement Learning(Value/Policy Iteration, Q-learning), from UC Berkeley CS188.

Projects are all listed above, have fun here.

• Latex教程
• sol_aima.pdf: solution to the book AIMA
• report_MATLAB_for_Machine_Learning_20191205
• T02_Answer.pdf
• Automated Machine Learning with Monte-Carlo Tree Search

• AI Summary: summary for review.

Record of AI course in SYSU, 8, 2019