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BEHAVIORAL ASSUMPTION-BASED PREDICTION F OR 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. The problem. Multi-player games Fast-paced racing

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behavioral assumption based prediction f or high latency hiding in mobile games

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

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

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France

the problem1

T = t1 : Transmit red position

Network

T = t1 + ΔTlatency : Receive position

Actual position red

Network

Received position red

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

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France

a common technique
A common technique

T = t1 : Transmit red position

  • Prediction
  • Prediction errors
  • Dead reckoning

Network

T = t1 + ΔTlatency : Receive position

Actual position red

Predicted position red

Network

Received position red at t1

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France

a common technique1
A common technique

T = t1 : Transmit red position

  • Prediction
  • Prediction errors
  • Dead reckoning

Network

T = t1 + ΔTlatency : Receive position

Actual position red

Predicted position red

Network

Received position red at t1

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France

mobile games
Mobile games
  • Platform: Java, BREW, Symbian, …
  • Network: GPRS using HTTP/TCP/IP stack

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France

prediction models
Prediction models

~

Xt

Xt

  • Standard strategy:
    • Only extrapolate from older data
  • Our approach:
    • Assume track following behavior
    • Or assume racing line optimizing behavior

Xt-1

Xt-2

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France

prediction models1
Prediction models

Xt

~

Xt

  • Standard strategy:
    • Only extrapolate from older data
  • Our approach:
    • Assume track following behavior
    • Or assume racing line optimizing behavior

Xt-1

Xt-2

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France

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

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France

continuity1
Continuity
  • Multiple simulations
    • Running 2 or 3 real-time simulations
    • Linear interpolation between simulations
    • Round robin

simA

simB

simC

weight

time

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France

a test drive
A Test Drive
  • Creating a testbed
  • Comparing results
  • Razor

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France

a test drive1
A Test Drive
  • Creating a testbed
  • Comparing results
  • Razor

http://www.exmachina.nl

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France

conclusions
Conclusions
  • Mobile 2.5G networks: a problematic domain
  • Standard dead reckoning is insufficient
  • Behavioral assumptions improve prediction
  • Running multiple simulations: a good fit

G.J.P. de Carpentier and R. Bidarra CGAMES 2005, France