38 results about "Partially observable Markov decision process" patented technology

The disclosure relates to an automatic brain-probe guidance systems. Specifically, the disclosure is directed to a real-time method and system for guiding a probe to the dorsolateral oscillatory region of the subthalamic nucleus in the brain of a subject in need thereof using factored Partially Observable Markov Decision Process (POMDP) via Hidden Markov Model (HMM) represented as a dynamic Bayesian Network (DBN).

The invention discloses a web service combination method based on deep reinforcement learning. Aiming at the problems of long time consumption, poor flexibility and unsatisfactory combination results of the traditional service combination method in the face of large-scale service scenarios, the deep reinforcement learning technology and heuristic Ideas applied to the problem of service composition. In addition, considering the characteristics of partial observability of the real environment, the present invention transforms the service composition process into a partially observable Markov decision process (Partially-Observable Markov Decision Process, POMDP), and utilizes a recurrent neural network to solve the problem of POMDP , so that the method can still show high efficiency in the face of the "curse of dimensionality" challenge. The method of the present invention can effectively improve the solution speed, and independently adapt to the dynamic service composition environment on the basis of ensuring the quality of the service composition scheme, effectively improving the efficiency and self-adaptability of the service composition in the large-scale dynamic service composition scene and flexibility.

In one embodiment of the invention, a training model for students is provided that models how to present training items to students in a computer based adaptive trainer. The training model receives student performance data and uses the training model to infer underlying student skill levels throughout the training sequence. Some embodiments of the training model also comprise machine learning techniques that allow the training model to adapt to changes in students skills as the student performs on training items presented by the training model. Furthermore, the training model may also be used to inform a training optimization model, or a learning model, in the form of a Partially Observable Markov Decision Process (POMDP).

The invention discloses a wireless Internet of Things resource allocation method based on probabilistic transfer deep reinforcement learning, which distributes decision-making agents in each edge server, so that each agent only needs to make decisions for the users it serves That is, the decision variable space is greatly reduced, and the decision delay is also reduced. At the same time, a service migration model based on a distributed partially observable Markov decision process is proposed, which overcomes the problem that each agent can observe The state information of the state is limited, so that the decision-making cannot reach the optimal solution.