BackgammonBackgammon
Group 1:
- Remco Bras
- Tim Beyer
- Maurice Hermans
- Esther Verhoef
- Thomas Acker
Introduction to backgammon
Demonstration
Game complexity
Different AI algorithms
Future plans
OverviewOverview
Two players
Pieces are moved according to the roll of dice
One player moves clockwise, othercounterclockwise
Pieces kicked off will be placed on the baulk
Scores when reached
Home Board
Introduction to backgammonIntroduction to backgammon
Let’s demonstrate!
DemonstrationDemonstration
Game ComplexityGame Complexity
Neural Network
◦The state of the board as input
layer
◦The evaluation function as
hidden layer
◦The “best move” as output layer
AI AlgorithmsAI Algorithms
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 AlgorithmsAI Algorithms
Pubeval
◦A benchmark “player” by Gerry Tesauro
◦Fixed weights Fixed strategy
◦Used very often to compare different approaches
◦Used for illustrating training effects
AI AlgorithmsAI Algorithms
Implement Neural networks
Vary training sessions
Future PlansFuture Plans
Week1
Week2
Week3
Week4
Week5
Week6
Week7
Research
ImplementAI
Testing anddebugging
Improve AI
Working onpresentation
Gannt-Chart