1. Backgammon Group 1: - Remco Bras - Tim Beyer - Maurice Hermans - Esther Verhoef - Thomas Acker

2.  Introduction to backgammon  Demonstration  Game complexity  Different AI algorithms  Future plans Overview

3.  Two players  Pieces are moved according to the roll of dice  One player moves clockwise, other counterclockwise  Pieces kicked off will be placed on the baulk  Scores when reached Home Board Introduction to backgammon

4. Let’s demonstrate! Demonstration

5. Game Complexity

6.  Neural Network ◦ The state of the board as input layer ◦ The evaluation function as hidden layer ◦ The “best move” as output layer AI Algorithms

7.  Reinforcement Learning (RL) ◦ Learning by playing matches against itself ◦ Temporal Difference Learning (TDL)  Changes after every time step  Temporal  Changes through differences  Difference  Learning of a evaluation (value) function  Learning AI Algorithms

8.  Pubeval ◦ A benchmark “player” by Gerry Tesauro ◦ Fixed weights  Fixed strategy ◦ Used very often to compare different approaches ◦ Used for illustrating training effects AI Algorithms

9.  Implement Neural networks  Vary training sessions Future Plans

10. Week1Week2Week3Week4Week5Week6Week7 Research Implement AI Testing and debugging Improve AI Working on presentation Gannt-Chart