Merging Indiscriminable Diagnoses: an Approach Based on Automatic Domains Abstraction

Abstract

The paper presents an approach suitable for on-line diagnosis, which aims at automatically abstracting the domains of discrete variables in the model (i.e., behavioral modes of system components) in order to keep only those distinctions that are relevant given the available observations and their granularity. In particular the paper describes an algorithm which identifies indistinguishable behavioral modes by taking into account specific classes of available observations and derives an abstract model where such modes are merged and the domain model is revised accordingly. By considering increasingly restricted classes of available observations (and/or granularity of observations), a set of abstract models can be derived that can be exploited through model selection each time a new diagnostic problem has to be solved. The approach has been tested within the framework of a diagnostic agent for a space robotic arm, and experimental results showing the reduction in the number of diagnoses are reported.

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Document Details

Document Type
Technical Report
Publication Date
May 04, 2002
Accession Number
ADP012692

Entities

People

  • Gianluca Torta
  • Pietro Torasso

Organizations

  • University of Turin

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Automatic
  • Case Studies
  • Coding
  • Computational Complexity
  • Content Addressable Memory
  • Digital Circuits
  • Electronic Mail
  • Experimental Data
  • Gas Turbines
  • Information Theory
  • Observation
  • Technical Information Centers
  • Test Sets
  • Workshops

Readers

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  • Computational Modeling and Simulation
  • Fault Tolerant Diagnosis of Black and White Balloon Isolation Tests Using ¥.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control
  • Space