let’s play snooker
DESCRIPTION
Group 2 Yannick Thimister Frans van den Heuvel Enno Ruijters Esther Verhoef Ali B. Ozmen Achim Leydecker. Let’s play snooker. Overview. Task description - Yannick Physics and noise – Ali Simple method – Frans Complex method - Enno Product demonstration - Esther - PowerPoint PPT PresentationTRANSCRIPT
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Let’s play snooker
Group 2Yannick ThimisterFrans van den HeuvelEnno RuijtersEsther VerhoefAli B. OzmenAchim Leydecker
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Overview
Task description - Yannick Physics and noise – Ali Simple method – Frans Complex method - Enno Product demonstration - Esther Experiments and results - Achim Conclusion - Yannick
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Task description
Design and implement an artificial intelligence for a Snooker simulation with a realistic physics model, including noise. Then determine the effects of this noise on the artificial intelligence.
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Physics
• Spin & Cue Ball• Impulse • Noise Model
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Spin and Cue ball
V: Linear velocity
F: Force acting on ball by cue
M: Mass of cue ball
θ: Angle between q and x-y plane
ω: Angular velocity
F: Force acting on ball by cue
M: Mass of cue ball
θ: Angle between q and x-y plane
R : Radius of the cue ball
...........0...........
cos /
...........0...........CBV Fx M
sin cos2
sin5
cos
Z y
Z CB
x
F F
F M R
F
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Impulse
• Collisions between balls are handled by adding a certain amount of impulsive force to both balls in opposite directions.
• The magnitude of this impulse is given by the equation ;
J = J =
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Impulse
• When we used to calculate the change in velocity in the collision, previous equation can be simplified into this equation:
• By assuming the two balls have the same mass, since the masses are factored out again when converting the change in momentum into the corresponding change in velocity.
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Noise Model
A set of standard deviations
• Five parameters specified
(4 Input parameter & Coefficient of friction )
• Error is modeled by perturbing these 5 parameters by zero – means Gaussian noise.
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Simple method
• Repeated random sampling• All directions / variation of force / No spin• Two versions• Iterate through the 50 best shots• Highest average amount of points• Easy implementation & good results
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Tactical method
• Extended version of Simple AI• Multithreaded
(4000 shots, 4 threads)• Adds heuristics to shot evaluation
– Avoid complex shots– Use tactics to improve future shots
• Naturally more resistant to noise
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Tactical method(complexity avoidance)
• Minimize number of collisions– Ignore irrelevant collisions– Collisions between two moving
balls strongly avoided
• Minimize distance traveled– Again, ignore irrelevant paths
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Tactical method (snooker tactic)
• Snooker if no potting possible Avoid snookering yourself Considers only direct snookers
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Tactical method(easy ball tactic)
• Try to leave easy balls for later shots
Avoid impossible shots later Problem: helping opponent?
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Tactical method(shot evaluation)
• Initial score based on actual points gained
• Add points for snookering opponent• Scale by penalty for every collision
– Exponential reduction• Scale linearly by distance• Add/subtract points for easy balls
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Program demonstration
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Experiments
Basic setup 100 full games per experiment
No player change Foul points are subtracted from the
total points Average and standard deviation are
recorded
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Experiments
Noise All four types of noise are tested
separately with five different degrees of intensity
Fixed maximum number of random samples
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Experiments
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Experiments
Number of random samples Fixed noise level (2)
Effects of heuristics for tactical method Fixed noise level (2) Each tactic is tested separately Collision-avoidance & distance-
minimization only Snookering is tested by playing against
the simple method with 500 random samples
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Results
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Results
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Conclusion
• Both methods decrease in performance when noise is added.
• The simple method performs better in general than the tactical method.
• In the tactical method, collision avoidance is less effective than distance minimization.
• Snookering is less effective than leaving easy balls.
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Questions