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We propose a new minimum uncertainty planning technique for mobile robots localizing with beacons. We model the system as a partially-observable Markov decision process and use a sampling-based method in the belief space (the space of posterior probability density functions over the state space) to find a belief-feedback policy. This approach allows us to analyze the evolution of the belief more accurately,...
In this work we consider the problem of automatically designing a penetration test plan that can be executed remotely, without prior knowledge of the target machine or network. We develop a methodology for generating and executing remote testing plans that takes into account the uncertainty of using remote tools both to gain knowledge of the system and to provide the penetration testing actions. Our...
This paper presents a new model to incorporate decision theory into Graphplan framework, which enables our planner to handle uncertainty and make decision to choose the optimal one among a set of hypothesis valid plans. This planer, called UTDP is tested on several experimental domains. And the experimental results show that UTGP is sound and efficient and performs better than the famous probabilistic...
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