BEHAVIORAL ASSUMPTION-BASED PREDICTION FOR HIGH-LATENCY HIDING IN MOBILE GAMES - PowerPoint PPT Presentation

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BEHAVIORAL ASSUMPTION-BASED PREDICTION FOR HIGH-LATENCY HIDING IN MOBILE GAMES

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BEHAVIORAL ASSUMPTION-BASED PREDICTION FOR HIGH-LATENCY HIDING IN MOBILE GAMES Giliam J.P. de Carpentier Rafael Bidarra Computer Graphics and CAD/CAM Group – PowerPoint PPT presentation

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Title: BEHAVIORAL ASSUMPTION-BASED PREDICTION FOR HIGH-LATENCY HIDING IN MOBILE GAMES


1
BEHAVIORAL ASSUMPTION-BASED PREDICTIONFOR
HIGH-LATENCY HIDING IN MOBILE GAMES
  • Giliam J.P. de Carpentier
  • Rafael Bidarra

Computer Graphics and CAD/CAM Group Faculty of
Electrical Engineering, Mathematics and Computer
Science
2
The problem
  • Multi-player games
  • Fast-paced racing
  • Network connections
  • Latency

3
The problem
  • Multi-player games
  • Fast-paced racing
  • Network connections
  • Latency

4
A common technique
T t1 Transmit red position
  • Prediction
  • Prediction errors
  • Dead reckoning

Network
T t1 ?Tlatency Receive position
Actual position red Predicted
position red
Network
Received position red at t1
5
A common technique
T t1 Transmit red position
  • Prediction
  • Prediction errors
  • Dead reckoning

Network
T t1 ?Tlatency Receive position
Actual position red
Predicted position red
Network
Received position red at t1
6
Mobile games
  • Platform Java, BREW, Symbian,
  • Network GPRS using HTTP/TCP/IP stack

LAN network (PC game) Internet (PC game) GPRS network (Mobile game)
Network latency 1 - 5 ms 50 - 200 ms 1500 - 3500 ms
Prediction distance in a race game_at_ 100 km/h 0.03 0.14 meter 1.4 - 5.6 meter 42 97 meter
Prediction error, assuming a max. of 10 deviation. lt 2 cm lt 0.6 meter lt 10 meter
Results Dead- reckoning Good. Medium. Interactions like collisions can be problematic. Bad. Errors as large as the width of a track are unacceptable.
7
Prediction models
Xt
  • Standard strategy
  • Only extrapolate from older data
  • Our approach
  • Assume track following behavior
  • Or assume racing line optimizing behavior

Xt
Xt-1
Xt-2
8
Prediction models
Xt
  • Standard strategy
  • Only extrapolate from older data
  • Our approach
  • Assume track following behavior
  • Or assume racing line optimizing behavior

Xt
Xt-1
Xt-2
9
Continuity
  • Multiple simulations
  • Running 2 or 3 real-time simulations
  • Linear interpolation between simulations
  • Round robin

10
Continuity
  • Multiple simulations
  • Running 2 or 3 real-time simulations
  • Linear interpolation between simulations
  • Round robin

simB
simA
simC
weight
time
11
A Test Drive
  • Creating a testbed
  • Comparing results
  • Razor

12
A Test Drive
  • Creating a testbed
  • Comparing results
  • Razor

http//www.exmachina.nl
13
Conclusions
  • Mobile 2.5G networks a problematic domain
  • Standard dead reckoning is insufficient
  • Behavioral assumptions improve prediction
  • Running multiple simulations a good fit
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