Using Neural-Networks to Reduce Entity State Updates in Distributed Interactive Applications

Abstract

Dead reckoning is the most commonly used predictive contract mechanism for the reduction of network traffic in Distributed Interactive Applications (DIAs). However, this technique often ignores available contextual information that may be influential to the state of an entity, sacrificing remote predictive accuracy in favour of low computational complexity. In this paper, we present a novel extension of dead reckoning by employing neuralnetworks to take into account expected future entity behaviour during the transmission of entity state updates (ESUs) for remote entity modeling in DIAs. This proposed method succeeds in reducing network traffic through a decrease in the frequency of ESU transmission required to maintain consistency. Validation is achieved through simulation in a highly interactive DIA, and results indicate significant potential for improved scalability when compared to the use of the IEEE DIS Standard dead reckoning technique. The new method exhibits relatively low computational overhead and seamless integration with current dead reckoning schemes.

Towards an Information Model of Consistency Maintenance in Distributed Interactive Applications

“…This superior performance generally comes from two factors: larger network traffic and more computational resource requirements. For instance, in Figure 7(a), the second-order extrapolation is extracting more information per time step from the latest ESU than the linear extrapolation, at the cost of a larger number of ESU transmissions (see Figure 7(b)) and more memory to restore longer referenced historical states and more computational resources to calculate the extrapolation from (14). Even though computational consumption is becoming more and more insignificant as computers are becoming more powerful, our model still provides a formulation of the ability of the prediction model to utilise ESUs by (10) and an explicit way to deal with the tradeoff between prediction accuracy and computational consumption.…”

“…In standard Dead Reckoning and its various extensions, multiple-order polynomial functions are used to extrapolate state evolution until the next ESU is generated [2,26,27]. More complicated methods involving statistical learning, such as Kalman filters [28] and Neural Networks [13,14], are employed to improve the performance of prediction. Whether having a closed form formula or not, these algorithms are essentially functions or mappings f (•) from the previous generated ESUs to the anticipated states in the future.…”

“…Here, state d(k) replaces d 1 (k) as a correction. For secondorder DR, extrapolated entity state and data in an ESU are given by (14) and (15), respectively,…”

“…Predictive contract mechanisms maintain controlled inconsistency, or a sufficient level of consistency, by using prediction schemes (e.g., Dead Reckoning [2,5,6], Hybrid Strategy Model [12], Nero-Reckoning [13,14], etc.) to explore information about the future motion of the objects 2 International Journal of Computer Games Technology from contextual dynamics and reduce the frequency of sending entity state updates (ESUs) from the local controlling host to the remote host across the network.…”