A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters

2013/01/16 by Claus Skaanning, Skaanning, Claus
Computer Science · Decision Sciences · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Quality and Management #FOS: Computer and information sciences #cs.AI

paper · pdf · doi:10.48550/arxiv.1301.3893

Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000)

arxiv created 2013/01/16 · openalex publication_date 2013/01/16 · arxiv updated 2013/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• A Polynomial Algorithm for Computing the Optimal Repair Strategy in a System with Independent Component Failures

Related

• A Case Study in Knowledge Discovery and Elicitation in an Intelligent Tutoring Application
• ExpertBayes: Automatically refining manually built Bayesian networks
• BaRT: A Bayesian Reasoning Tool for Knowledge Based Systems
• On the Detection of Conflicts in Diagnostic Bayesian Networks Using Abstraction
• Bayesian Generalized Network Design