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# Artificial Intelligence I: Meta-Heuristics and Games in Java

Graph Algorithms, Genetic Algorithms, Simulated Annealing, Swarm Intelligence, Minimax, Heuristics and Meta-Heuristics
Get a good grasp of artificial intelligence
Understand how AI algorithms work
Understand graph search algorithms - BFS, DFS and A* search
Understand meta-heuristics
Understand genetic algorithms
Understand simulated annealing
Understand swarm intelligence and particle swarm optimization
Understand game trees
Understand minimax algorithm and alpha-beta pruning
Tic Tac Toe game from scratch with minimax algorithm

This course is about the fundamental concepts of artificial intelligence. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detecting cancer for example. We may construct algorithms that can have a very  good guess about stock price movement in the market.

– PATHFINDING ALGORITHMS –

Section 1 – Breadth-First Search (BFS)

• what is breadth-first search algorithm

• why to use graph algorithms in AI

Section 2 – Depth-First Search (DFS)

• what is depth-first search algorithm

• implementation with iteration and with recursion

• depth-first search stack memory visualization

• maze escape application

Section 3 – Iterative Deepening Depth-First Search (IDDFS)

• what is iterative deepening depth-first search algorithm

Section 4 – A* Search Algorithm

• what is A* search algorithm

• what is the difference between Dijkstra’s algorithm and A* search

• what is a heuristic

• Manhattan distance and Euclidean distance

– OPTIMIZATION –

Section 5 – Optimization Approaches

• basic optimization algorithms

• brute-force search

• hill climbing algorithm

– META-HEURISTICS –

Section 6 – Tabu Search

• what is the tabu search algorithm

• tabu tenure and aspiration criteria

Section 7 – Simulated Annealing

• what is simulated annealing

• how to find the extremum of functions

• how to solve combinatorial optimization problems

• travelling salesman problem (TSP)

Section 8 – Genetic Algorithms

• what are genetic algorithms

• artificial evolution and natural selection

• crossover and mutation

• solving the knapsack problem

Section 9 – Particle Swarm Optimization (PSO)

• what is swarm intelligence

• what is the Particle Swarm Optimization algorithm

– GAMES AND GAME TREES –

Section 10 – Game Trees

• what are game trees

• how to construct game trees

Section 11 – Minimax Algorithm and Game Engines

• what is the minimax algorithm

• what is the problem with game trees?

• using the alpha-beta pruning approach

• chess problem

Section 12 – Tic Tac Toe with Minimax

• Tic Tac Toe game and its implementation

• using minimax algorithm

In the first chapter we are going to talk about the basic graph algorithms. Several advanced algorithms can be solved with the help of graphs, so as far as I am concerned these algorithms are the first steps.

Second chapter is about local search: finding minimum and maximum or global optimum in the main. These searches are used frequently when we use regression for example and want to find the parameters for the fit. We will consider basic concepts as well as the more advanced algorithms: heuristics and meta-heuristics.

The last topic will be about minimax algorithm and how to use this technique in games such as chess or tic-tac-toe, how to build and construct a game tree, how to analyze these kinds of tree like structures and so on. We will implement the tic-tac-toe game together in the end.

Thanks for joining the course, let’s get started!

1
Introduction

### Why Should You Learn Artificial Intelligence?

1
What is AI good for?

### PATHFINDING ALGORITHMS (GRAPHS)

1
Why to consider graph algorithms?

1
2
3
BFS Quiz

### Depth-First Search (DFS)

1
Depth-first search introduction
2
Depth-first search implementation I - with stack
3
Depth-first search implementation II - with recursion
4
Depth-first search and stack memory visualisation
5
DFS Quiz

### Course Challenge #1 - Maze Escape

1
Maze problem introduction
2
Course challenge #1 - maze problem
3
Maze problem implementation

### Iterative Deepening Depth-First Search (IDDFS)

1
Enhanced search algorithms introduction (IDDFS)
2
Iterative deepening depth-first search (IDDFS) implementation
3
Enhanced Search Quiz

### A* Search Algorithm

1
A* search introduction
2
A* search illustration
3
A* search implementation I
4
A* search implementation II
5
A* search implementation III
6
Path finding algorithms comparison
7
A* Search Quiz

### OPTIMIZATION

1
Brute-force method
2
Brute-force method implementation
3
Hill climbing method
4
Hill climbing method implementation
5
Optimization Quiz

### META-HEURISTICS AND OPTIMIZATION

1
Heuristics and meta-heuristics
2
Heuristics Quiz

### Tabu Search

1
Tabu search introduction - basics
2
Tabu search introduction - tabu tenure
3
Tabu search illustration
4
Tabu search implementation I
5
Tabu search implementation II
6
Tabu Search Quiz

### Simulated Annealing

1
What is simulated annealing?
2
Simulated annealing - function extremum I
3
Simulated annealing - function extremum II
4
Simulated annealing - function extremum III
5
What is the travelling salesman problem?
6
Travelling salesman problem I - city
7
Travelling salesman problem II - tour
8
Travelling salesman problem III - annealing algorithm
9
Travelling salesman problem IV - testing
10
Simulated Annealing Quiz

### Genetic Algorithms

1
Genetic algorithms introduction - basics
2
Genetic algorithms introduction - chromosomes
3
Genetic algorithms introduction - crossover
4
Genetic algorithms introduction - mutation
5
Genetic algorithms introduction - the algorithm
6
Genetic algorithm implementation I - individual
7
Genetic algorithm implementation II - population
8
Genetic algorithm implementation III - the algorithm
9
Genetic algorithm implementation IV - testing
10
Genetic algorithm implementation V - function optimum
11
Genetic Algorithms Quiz

### Course Challenge #2 - Knapsack Problem

1
Knapsack problem introduction
2
Course challenge #2 - knapsack problem
3
Knapsack problem with genetic algorithms

### Particle Swarm Optimization

1
What is swarm intelligence?
2
Particle swarm optimization introduction I - basics
3
Particle swarm optimization introduction II - the algorithm
4
Particle swarm optimization implementation I - particle
5
Particle swarm optimization implementation II - initialize
6
Particle swarm optimization implementation III - the algorithm
7
Particle swarm optimization implementation IV - testing
8
Particle Swarm Optimization Quiz

### TWO PLAYER GAMES

1
Game trees introduction
2
Two Player Games Quiz

### Minimax Algorithm - Game Engines

1
Minimax algorithm introduction - basics
2
Minimax algorithm introduction - the algorithm
3
Minimax algorithm introduction - relation to tic-tac-toe
4
Alpha-beta pruning introduction
5
Alpha-beta pruning example
6
Chess problem
7
Game Engines Quiz

### Tic-Tac-Toe Game

1
2
Cell
3
Constants and Player
4
Game implementation I
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Artificial Intelligence I: Meta-Heuristics and Games in Java
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