Investigating Behavioural State Data-Partitioning for User-Modelling in Distributed Interactive Applications

McCoy, Aaron; McLoone, Séamus; Ward, Tomás E.; Delaney, Declan

doi:10.1109/ds-rt.2004.26

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…The data from this genre of games can be considered a fair representation of the type of data one would expect to see in commercial networked multiplayer computer games [6].…”

Section: The Application Domainmentioning

confidence: 99%

“…In order to collect the type of data that one would expect to observe in a real-world DIA (and to illustrate the potential of neuro-reckoning for network traffic reduction), we utilize the commercially available Torque Game Engine [11] as a customisable research platform for performing experiments in a controlled manner [6]. We present results for three human-user test subjects (each possessing a varying degree of expertise with respect to networked multiplayer computer games -Test Subject 1 being the most experienced, Test Subject 3 the least), who were each required to individually compete in a series of 12 consecutive experiments (consisting of a pre-specified 'hit-point' score limit of 10) against a scripted computercontrolled opponent operating under the influence of a simple dynamic shortest-path behavioural model [12].…”

Section: Experimentationmentioning

confidence: 99%

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

McCoy

Ward

McLoone

et al. 2006

2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing

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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.

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“…The data from this genre of games can be considered a fair representation of the type of data one would expect to see in commercial networked multiplayer computer games [6].…”

Section: The Application Domainmentioning

confidence: 99%

Section: Experimentationmentioning

confidence: 99%

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

McCoy

Ward

McLoone

et al. 2006

2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing

View full text Add to dashboard Cite

show abstract

“…This strategy was developed using the commercially available torque game engine (TGE) [Lloyd 2004]. The rules that govern the game are fairly typical of the type of "deathmatch" scenarios used within online FPS games, and yield data that can be considered a fair representation for the type of data that one would expect to observe in commercial networked multiplayer computer games and other similar real-time applications [McCoy et al 2004]:…”

Section: Overviewmentioning

confidence: 99%

Multistep-ahead neural-network predictors for network traffic reduction in distributed interactive applications

McCoy

Ward

McLoone

et al. 2007

ACM Trans. Model. Comput. Simul.

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Predictive contract mechanisms such as dead reckoning are widely employed to support scalable remote entity modeling in distributed interactive applications (DIAs). By employing a form of controlled inconsistency, a reduction in network traffic is achieved. However, by relying on the distribution of instantaneous derivative information, dead reckoning trades remote extrapolation accuracy for low computational complexity and ease-of-implementation. In this article, we present a novel extension of dead reckoning, termed neuro-reckoning, that seeks to replace the use of instantaneous velocity information with predictive velocity information in order to improve the accuracy of entity position extrapolation at remote hosts. Under our proposed neuro-reckoning approach, each controlling host employs a bank of neural network predictors trained to estimate future changes in entity velocity up to and including some maximum prediction horizon. The effect of each estimated change in velocity on the current entity position is simulated to produce an estimate for the likely position of the entity over some short time-span. Upon detecting an error threshold violation, the controlling host transmits a predictive velocity vector that extrapolates through the estimated position, as opposed to transmitting the instantaneous velocity vector. Such an approach succeeds in reducing the spatial error associated with remote extrapolation of entity state. Consequently, a further reduction in network traffic can be achieved. Simulation results conducted using several human users in a highly interactive DIA indicate significant potential for improved scalability when compared to the use of IEEE DIS standard dead reckoning. Our proposed neuro-reckoning framework exhibits low computational resource overhead for real-time use and can be seamlessly integrated into many existing dead reckoning mechanisms. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212)

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Investigating Behavioural State Data-Partitioning for User-Modelling in Distributed Interactive Applications

Cited by 2 publications

References 10 publications

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

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

Multistep-ahead neural-network predictors for network traffic reduction in distributed interactive applications

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