Learning for User Adaptive Systems: Likely Pitfalls and Daring Rescue
نویسنده
چکیده
Adaptive user interfaces adapt themselves to the user by reasoning about the user and refining their internal model of the user’s needs. In machine learning, artificial systems learn how to perform better through experience. By observing examples from a sample, the learning algorithm tries to induce a hypothesis which approximates the target function. It seems obvious, that machine learning exactly offers what is desperately needed in intelligent adaptive behavior. But when trying to adapt by learning, one will sooner or later encounter one or more well–known problems, some of which have been discussed in [Webb et al., 2001]. We propose a framework for describing user modeling problems, identify several reasons for inherent noise and discuss few promising approaches which tackle these problems.
منابع مشابه
RESCUE: Reputation based Service for Cloud User Environment
Exceptional characteristics of Cloud computing has replaced all traditional computing. With reduced resource management and without in-advance investment, it has been victorious in making the IT world to migrate towards it. Microsoft announced its office package as Cloud, which can prevent people moving from Windows to Linux. As this drift is escalating in an exponential rate, the cloud environ...
متن کاملHybrid Adaptive Educational Hypermedia Recommender Accommodating User’s Learning Style and Web Page Features
Personalized recommenders have proved to be of use as a solution to reduce the information overload problem. Especially in Adaptive Hypermedia System, a recommender is the main module that delivers suitable learning objects to learners. Recommenders suffer from the cold-start and the sparsity problems. Furthermore, obtaining learner’s preferences is cumbersome. Most studies have only focused...
متن کاملReinforcement Learning Based PID Control of Wind Energy Conversion Systems
In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. Theadaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...
متن کاملADAPTIVE FUZZY TRACKING CONTROL FOR A CLASS OF PERTURBED NONLINEARLY PARAMETERIZED SYSTEMS USING MINIMAL LEARNING PARAMETERS ALGORITHM
In this paper, an adaptive fuzzy tracking control approach is proposed for a class of single-inputsingle-output (SISO) nonlinear systems in which the unknown continuous functions may be nonlinearlyparameterized. During the controller design procedure, the fuzzy logic systems (FLS) in Mamdani type are applied to approximate the unknown continuous functions, and then, based on the minimal learnin...
متن کاملAdaptive Approximation-Based Control for Uncertain Nonlinear Systems With Unknown Dead-Zone Using Minimal Learning Parameter Algorithm
This paper proposes an adaptive approximation-based controller for uncertain strict-feedback nonlinear systems with unknown dead-zone nonlinearity. Dead-zone constraint is represented as a combination of a linear system with a disturbance-like term. This work invokes neural networks (NNs) as a linear-in-parameter approximator to model uncertain nonlinear functions that appear in virtual and act...
متن کامل