Causality explanation generation based on multi-level flow model for operator support systems

Akio Gofuku, Yaoyang Zheng

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)

Abstract

It is important for an operator support system to generate an understandable explanation of its complex reasoning process in order to mutually interact with human operators. As a technique to explain the causality inference results based on detailed Multi-level Flow Modelling (MFM) models, a technique to generate essential explanation is studied by applying the technique called function flow simplification to simplify a detailed MFM model by a macroscopic view. Through several explanation generation results for effect estimation of counter actions, the technique is confirmed to generate almost suitable explanation sentences although several differences are found compared with the explanation given by plant experts.

Original languageEnglish
Pages538-541
Number of pages4
Publication statusPublished - Dec 1 2005
EventSICE Annual Conference 2005 - Okayama, Japan
Duration: Aug 8 2005Aug 10 2005

Other

OtherSICE Annual Conference 2005
CountryJapan
CityOkayama
Period8/8/058/10/05

Keywords

  • Causality explanation
  • Function flow simplification
  • Multi-level flow modelling
  • Operator support system

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this