Aaron McCoy

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

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.

Game-state fidelity across distributed interactive games

Distributed interactive games offer players a three dimensional virtual world experience. Within this virtual world, players interact with each other and with their environment in real-time. They experience the same events, but from different viewpoints. As interactive games have evolved, they have driven the technologies underlying them. The distinguishing feature of any distributed interactive game is the network: the medium by which information is exchanged and shared between participants. The network impacts not only the design and development of distributed interactive games, but also their potential entertainment value. In this article, four of the fundamental networking issues and their effect on the design and operation of distributed interactive games will be discussed. In addition, a description of the different communication architectures used in distributed interactive games will be provided. Finally, as an illustrative example, these issues will be related to Unreal Tournament, a popular distributed interactive game.

MindMusic: Playful and Social Installations at the Interface Between Music and the Brain

Single- and multi-agent installations and performances that use physiological signals to establish an interface between music and mental states can be found as early as the mid-1960s. Among these works, many have used physiological signals (or inferred cognitive, sensorimotor or affective states) as media for music generation and creative expression. To a lesser extent, some have been developed to illustrate and study effects of music on the brain. Historically, installations designed for a single participant are most prevalent. Less common are installations that invite participation and interaction between multiple individuals. Implementing such multi-agent installations raises unique challenges, but also unique possibilities for social interaction. Advances in unobtrusive and/or mobile devices for physiological data acquisition and signal processing, as well as computational methods for inferring mental states from such data, have expanded the possibilities for real-world, multi-agent, brain–music interfaces. In this chapter, we examine a diverse selection of playful and social installations and performances, which explore relationships between music and the brain and have featured publically in Mainly Mozart’s annual Mozart & the Mind (MATM) festival in San Diego. Several of these installations leverage neurotechnology (typically novel wearable devices) to infer brain states of participants. However, we also consider installations that solely measure behavior as a means of inferring cognitive state or to illustrate a principle of brain function. In addition to brief overviews of implementation details, we consider ways in which such installations can be useful vehicles, not only for creative expression, but also for education, social interaction, therapeutic intervention, scientific and aesthetic research, and as playful vehicles for exploring human–human and human–machine interaction.

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

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 neural- networks 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.

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

Distributed Interactive Applications (DIAs) have been gaining commercial success in recent years due to the widespread appeal of networked multiplayer computer games. Within these games, participants interact with each other and their environment, producing complex behavioural patterns that evolve over time. These patterns are non-linear, and often appear to exhibit dependencies under certain conditions. In this paper, we analyse the behavioural patterns of two users interacting in a DIA. Our motivation behind this analysis is to construct models of user behaviour that can be used within Entity-State-Update (ESU) mechanisms. By representing their behaviour as time-series datasets, we investigate the use of simple statistical dependence measures to help partition the datasets and identify three different types of behavioural states exhibited by the two users. It is our intention that future research on ESU mechanisms can utilize this behavioural partitioning to reduce the network traffic in a DIA based on a hybrid-model approach.

An Adaptive Rate-Based Method for Maintaining Consistency in Networked Multiplayer Computer Games

This paper presents a dynamic, rate-based method that regulates consistency, in response to changes in the underlying network, for client-server-based Multiplayer Computer Games. It operates on the premise that, as the network conditions between client and server changes, so too does the inconsistency from the clients viewpoint. Hence, adapting the rate of updates between the server and the client can help maintain an acceptable level of consistency.