Adaptive Selection of Intelligent Processing Modules and its Applications
نویسندگان
چکیده
In this paper we study the problem of applicationbased Human-Robot Interaction (HRI). We introduce a problem called The Human State Problem (HSP) and we propose a robotic architecture that partially solves this problem. In the HSP the goal is to keep a user that interacts with a robotic application in a desired state; in most cases this state is happy or satisfied. The robotic application uses real world feedback to reconfigure its behavior. The behavior is generated by a selection mechanism that adaptively selects computational resources that are then used for the processing of the current input-to-output mapping. The computational resources are selected from a pool of available intelligent processing resources that represents all available computational capacity of the robotic application. The main problem is in the fact that the robotic application receives only indirect and partial human feedback. Such feedback is not sufficient for the robot to easily predict or decide what actions are the most appropriate.
منابع مشابه
Statistical Wavelet-based Image Denoising using Scale Mixture of Normal Distributions with Adaptive Parameter Estimation
Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wa...
متن کاملMLIFT: Enhancing Multi-label Classifier with Ensemble Feature Selection
Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...
متن کاملFast SFFS-Based Algorithm for Feature Selection in Biomedical Datasets
Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...
متن کاملIntegration of intelligent systems in development of smart adaptive systems
Smart adaptive systems provide advanced tools for monitoring, control, diagnostics and management of nonlinear multivariate processes. Data mining with a multitude of methodologies is a good basis for the integration of intelligent systems. Small, specialised systems have a large number of feasible solutions, but highly complex systems require domain expertise and more compact approaches at the...
متن کاملParleda: a Library for Parallel Processing in Computational Geometry Applications
ParLeda is a software library that provides the basic primitives needed for parallel implementation of computational geometry applications. It can also be used in implementing a parallel application that uses geometric data structures. The parallel model that we use is based on a new heterogeneous parallel model named HBSP, which is based on BSP and is introduced here. ParLeda uses two main lib...
متن کامل