A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters

نویسنده

  • Claus Skaanning
چکیده

This paper describes a domain-specific knowledge acquisition tool for intelligent automated troubleshooters based on Bayesian networks. No Bayesian network knowledge is required to use the tool. and troubleshooting information can be specified as natural and intuitive as possible. Probabilities can be specified in the direction that is most natural to the domain expert. Thus, the knowledge acquisition efficiently removes the traditional knowledge acquisition bottleneck of Bayesian networks.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Automated Troubleshooting of Mobile Networks Using Bayesian Networks

In the current telecommunication scenarios operators have to cope with fast technological changes while increasing operational efficiency, i.e. diminishing operational expenditures and, at the same time, maximising performance of the networks. In this paper we present an automated troubleshooting tool for cellular networks, based on Bayesian networks, which will contribute to improve operationa...

متن کامل

KARaCAs: Knowledge Acquisition with Repertory Grids and Formal Concept Analysis for Dialog System Construction

We describe a new knowledge acquisition tool that enabled us to develop a dialog system recommending software design patterns by asking critical questions. This assistance system is based on interviews with experts. For the interviews we adopted the repertory grid method and integrated formal concept analysis. The repertory grid method stimulates the generation of common and differentiating att...

متن کامل

Using Bayesian Networks as an Inference Engine in KAMET

During the past decades, many methods have been developed for the creation of Knowledge-Based Systems (KBS). For these methods, probabilistic networks have shown to be an important tool to work with probability-measured uncertainty. However, quality of probabilistic networks depends on a correct knowledge acquisition and modelation. KAMET1is a model-based methodology designed to manage knowledg...

متن کامل

Creating a Bayesian Inference Engine for KAMET

Probabilistic networks have shown to be an important tool to work with probability-measured uncertainty. However, quality of probabilistic networks depends on a correct knowledge acquisition and modelling. KAMET is a model-based methodology designed to manage knowledge acquisition from multiple knowledge sources [1]. After this methodology is applied, a graphical model representing causal relat...

متن کامل

A Greedy Knowledge Acquisition Method for the Rapid Prototyping of Bayesian Belief Networks

Bayesian belief networks (BBNs) are a standard tool for building intelligent systems in domains with uncertainty for diagnostics, therapy planning and usermodelling. Modelling their qualitative and quantitative parts requires sometimes subjective data acquired from domain experts. This can be very time consuming and stressful causing a knowledge acquisition bottleneck. The main goal of this pap...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2000