The Department of Defense High Level Architecture
Proceedings of the 29th conference on Winter simulation
An auto-adaptive dead reckoning algorithm for distributed interactive simulation
PADS '99 Proceedings of the thirteenth workshop on Parallel and distributed simulation
Networked virtual environments: design and implementation
Networked virtual environments: design and implementation
How to Keep a Dead Man from Shooting
IDMS '00 Proceedings of the 7th International Workshop on Interactive Distributed Multimedia Systems and Telecommunication Services
Adaptive Attitude Dead-Reckoning by Cumulative Polynomial Extrapolation of Quaternions
DS-RT '01 Proceedings of the Fifth IEEE International Workshop on Distributed Simulation and Real-Time Applications
Using a Position History-Based Protocol for Distributed Object Visualization
Using a Position History-Based Protocol for Distributed Object Visualization
Evaluation of a Pre-Reckoning Algorithm for Distributed Virtual Environments
ICPADS '04 Proceedings of the Parallel and Distributed Systems, Tenth International Conference
CW '06 Proceedings of the 2006 International Conference on Cyberworlds
The SIMNET virtual world architecture
VRAIS '93 Proceedings of the 1993 IEEE Virtual Reality Annual International Symposium
A bayesian model to smooth telepointer jitter
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
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Most dead reckoning implementations are based on DIS' specifications and only use a single prediction model during the whole simulation. However, several studies manage to improve dead reckoning's performance by defining prediction model selection strategies. Nevertheless, these approaches are either too generic and based on empirical results or too specific and only have few fields of application. This paper presents our approach that is meant to determine, among a set of extrapolation models, the one to apply in any given situation. It is based on classifier systems, adaptive evolutionary systems that are more generally involved to create artificial creatures in the field of "artificial life". Using such systems enable us to define a general model that can generate simulation-specific rules with relatively little work. Indeed, they just require defining the parameters that have to be taken into account and the criteria to optimize (e.g. accuracy, amount of updates...). Then, the system makes a set of rules emerge through a trial/error process in order to define more efficient and finer prediction model selection strategies.