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Artificial Intelligence I: Meta-Heuristics and Games in Java

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Artificial Intelligence I: Meta-Heuristics and Games in Java

Artificial Intelligence I: Meta-Heuristics and Games in Java

Graph Algorithms, Genetic Algorithms, Simulated Annealing, Swarm Intelligence, Minimax, Heuristics and Meta-Heuristics

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What you'll learn

  • 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.


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


Section 5 - Optimization Approaches

basic optimization algorithms

brute-force search

hill climbing algorithm


Section 6 - Simulated Annealing

what is simulated annealing

how to find the extremum of functions

how to solve combinatorial optimization problems

travelling salesman problem (TSP)

Section 7 - Genetic Algorithms

what are genetic algorithms

artificial evolution and natural selection

crossover and mutation

solving the knapsack problem

Section 8 - Particle Swarm Optimization (PSO)

what is swarm intelligence

what is the Particle Swarm Optimization algorithm


Section 9 - Game Trees

what are game trees

how to construct game trees

Section 10 - 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 11 - 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!

Who this course is for:

  • This course is meant for students or anyone who interested in algorithms and optimization approaches and have some background in basic Java
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