Bounding inconsistency using a novel threshold metric for dead reckoning update packet generation

Human-to-human interaction across distributed applications requires that sufficient consistency be maintained among participants in the face of network characteristics such as latency and limited bandwidth. The level of inconsistency arising from the network is proportional to the network delay, and thus a function of bandwidth consumption. Distributed simulation has often used a bandwidth reduction technique known as dead reckoning that combines approximation and estimation in the communication of entity movement to reduce network traffic, and thus improve consistency. However, unless carefully tuned to application and network characteristics, such an approach can introduce more inconsistency than it avoids. The key tuning metric is the distance threshold. This paper questions the suitability of the standard distance threshold as a metric for use in the dead reckoning scheme. Using a model relating entity path curvature and inconsistency, a major performance related limitation of the distance threshold technique is highlighted. We then propose an alternative time—space threshold criterion. The time—space threshold is demonstrated, through simulation, to perform better for low curvature movement. However, it too has a limitation. Based on this, we further propose a novel hybrid scheme. Through simulation and live trials, this scheme is shown to perform well across a range of curvature values, and places bounds on both the spatial and absolute inconsistency arising from dead reckoning

Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version)

Many exact and approximate solution methods for Markov Decision Processes (MDPs) attempt to exploit structure in the problem and are based on factorization of the value function. Especially multiagent settings, however, are known to suffer from an exponential increase in value component sizes as interactions become denser, meaning that approximation architectures are restricted in the problem sizes and types they can handle. We present an approach to mitigate this limitation for certain types of multiagent systems, exploiting a property that can be thought of as "anonymous influence" in the factored MDP. Anonymous influence summarizes joint variable effects efficiently whenever the explicit representation of variable identity in the problem can be avoided. We show how representational benefits from anonymity translate into computational efficiencies, both for general variable elimination in a factor graph but in particular also for the approximate linear programming solution to factored MDPs. The latter allows to scale linear programming to factored MDPs that were previously unsolvable. Our results are shown for the control of a stochastic disease process over a densely connected graph with 50 nodes and 25 agents.Comment: Extended version of AAAI 2016 pape