Updating user profile using ontology based semantic similarity online dating sites older people

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However, PILCO applies policies to the observed state, therefore planning in observation space.

We extend PILCO with filtering to instead plan in belief space, consistent with partially observable Markov decisions process (POMDP) planning. Abstract: This paper presents three iterative methods for orientation estimation.

The adaptive E-learning system focuses on how the profile data is learned by the learner and pays attention to learning activities, cognitive structures and the context of the learning material.

The system controls the process of collecting data about the learner, the process of acquiring the learner profile and during the adaptation process.

The Learning style of the learner can be acquired by using the learner behavior during utilizing the E-learning system.

e-Learning is one of the most preferred media of learning by the learners.

The results of the proposed e-Learning system under the designed cross ontology similarity measure show a significant increase in performance and accuracy under different conditions.

The proposed framework is experimentally validated using synthetic and real-world datasets.

We introduce matrices with complex entries which give significant further accuracy improvement.

We provide geometric and Markov chain-based perspectives to help understand the benefits, and empirical results which suggest that the approach is helpful in a wider range of applications. Data-efficient reinforcement learning in continuous state-action Gaussian-POMDPs. Abstract: We present a data-efficient reinforcement learning method for continuous state-action systems under significant observation noise.

This enables data-efficient learning under significant observation noise, outperforming more naive methods such as post-hoc application of a filter to policies optimised by the original (unfiltered) PILCO algorithm. On orientation estimation using iterative methods in Euclidean space. The first two are based on iterated Extended Kalman filter (IEKF) formulations with different state representations.

We test our method on the cartpole swing-up task, which involves nonlinear dynamics and requires nonlinear control. The first is using the well-known unit quaternion as state (q-IEKF) while the other is using orientation deviation which we call IMEKF.

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