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This paper presents a new robust and adaptive framework for Markov decision processes that accounts for errors in the transition probabilities. Robust policies are typically found off-line, but can be extremely conservative when implemented in the real system. Adaptive policies, on the other hand, are specifically suited for on-line implementation, but may display undesirable transient performance...
This article has presented a tightly integrated systems architecture for a decentralized CSAT mission management algorithm and demonstrated successful implementation in actual hardware flight tests. This CSAT architecture allows each UAV to accomplish a combined search and track mission by conceptualizing the searching aspect as a spare time strategy to be executed optimally over a short time horizon...
Many decision systems rely on a precisely known Markov Chain model to guarantee optimal performance, and this paper considers the online estimation of unknown, non-stationary Markov Chain transition models with perfect state observation. In using a prior Dirichlet distribution on the uncertain rows, we derive a mean-variance equivalent of the maximum a posteriori (MAP) estimator. This recursive mean-variance...
This paper presents a new robust decision making algorithm that accounts for model uncertainty in finite state/action, Markov Decision Processes (MDPs). In particular we generate robust and optimal control policies using Sigma Point sampling methods for dynamic multi-stage problems where the probabilistic transition model of the MDP may be fixed, but uncertain. In the case of poorly known transition...
This paper presents a new formulation for the UAV task assignment problem for uncertain dynamic environments. Uncertainty in this time-varying information directly implies that the optimization data, such as target cost and target-UAV distances, will be uncertain. To mitigate the impact of this uncertainty, the new algorithm combines two key approaches that have been developed to handle the changes...
This paper investigates the problem of search for moving targets when the target motion is poorly known. The approach taken is probabilistic, modeling the target motion with a discrete-state, discrete-time Markov chain-like model. The target motion across the discretized environment is described by a probabilistic state transition matrix. Because the target motion at each time step will not be known...
This paper extends a recently developed statistical framework for UAV search with uncertain probability maps to the case of dynamic targets. The probabilities used to encode the information about the environment are typically assumed to be exactly known in the search theory literature, but they are often the result of prior information that is both erroneous and delayed, and will likely be poorly...
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