The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Learning and planning in partially bservable Markov decision processes (POMDPs) is computationally intractable in real-time system. In order to address this problem, this paper proposes a belief policy reuse (BPR) method to avoid repeated computation. Firstly, the policy reuse evaluation mechanism based on belief Kullback¨CLeibler divergence is presented as a similarity metric between beliefs in the...
Bayesian reinforcement learning provides an elegant solution to the optimal tradeoff between exploration and exploitation of the uncertainty in learning. Unfortunately, the size of the learning parameters grows exponentially with the problem horizon. In this paper, we propose a novel Monte Carlo tree search for Bayesian reinforcement learning approach using a compact factored representation, to solve...
Modern manufacturing systems are human robot systems that consist of human operators and intelligent robots collaborating with each other to accomplish complex tasks. The system performance of such human robot systems relies heavily on reliable and efficient human robot collaborations, which may be seriously compromised due to temporal variations in human to robot trust. This paper proposes to model...
Partially Observable Markov Decision Process (POMDP) has been widely used in the robotics to model uncertainties from sensors, actuators and the environment. However, such comprehensiveness makes the planning in POMDP generally very difficult. Existing work often searches for an optimal control policy with respect to predefined reward functions, which may require a large memory and is computationally...
Formal methods in robotic motion planning have emerged as a hot research topic recently due to its correct-by-design nature, and most results haven been based on nonprobabilistic discrete models. To better handle the environment uncertainties, sensor noise and actuator imperfection, control problems in probabilistic systems like Markov Chain (MC) and Markov Decision Process (MDP) have also been studied...
Wireless networked control systems (WNCS) with the control loops closed over a wireless network are prevailing these days. But it also produces new challenges for stability analysis when considering the nuance of the practical communication protocols. The IEEE 802.15.4 protocol has been very popular among communication protocols utilized in WNCS. However, usually its medium access control (MAC) is...
In this paper, we present a probabilistic framework for automatic detection and tracking of objects. We address the data association problem by formulating the visual tracking as finding the best partition of a measurement graph containing all detected moving regions. In order to incorporate model information in tracking procedure, the posterior distribution is augmented with Adaboost image likelihood...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.