Mobile agents can optimize their communication patterns to reduce bandwidth and latency and can adapt to changes in network service. We report on use of the Knowbot Operating Environment to support mobile agents in a wide-area network. Experiments with an application that monitors Web pages for changes show that a mobile program can outperform its stationary counterpart. The performance benefit...

In this paper we introduce the class of stationary prediction strategies and construct a prediction algorithm that asymptotically performs as well as the best continuous stationary strategy. We make mild compactness assumptions but no stochastic assumptions about the environment. In particular, no assumption of stationarity is made about the environment, and the stationarity of the considered s...

We study the target tracking problem using multiple, environment-embedded, stationary sensors and mobile robots. The stationary sensors and robots maintain region-based density estimates which are used to guide the robots to parts of the environment where unobserved targets may be present. Experiments in simulation show that the region-based approach works better than a ‘naive’ target following...

this paper presents a new formulation for warehouse inventory management in a stochastic situation. the primary source of this formulation is derived from fp model, which has been proposed by fletcher and ponnambalam for reservoir management. the new proposed mathematical model is based on the first and the second moments of storage as a stochastic variable. using this model, the expected value...

We present an approach to reward maximiza-tion in a non-stationary mobile robot environment. The approach works within the realistic constraints of limited local sensing and limited a priori knowledge of the environment. It is based on the use of augmented Markov models (AMMs), a general modeling tool we have developed. AMMs are essentially Markov chains having additional statistics associated ...