A tutorial on uncertainty modeling for machine reasoning

Branko Ristic, Christopher Gilliam, Marion Byrne, Alessio Benavoli

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Increasingly we rely on machine intelligence for reasoning and decision making under uncertainty. This tutorial reviews the prevalent methods for model-based autonomous decision making based on observations and prior knowledge, primarily in the context of classification. Both observations and the knowledge-base available for reasoning are treated as being uncertain. Accordingly, the central themes of this tutorial are quantitative modeling of uncertainty, the rules required to combine such uncertain information, and the task of decision making under uncertainty. The paper covers the main approaches to uncertain knowledge representation and reasoning, in particular, Bayesian probability theory, possibility theory, reasoning based on belief functions and finally imprecise probability theory. The main feature of the tutorial is that it illustrates various approaches with several testing scenarios, and provides MATLAB solutions for them as a supplementary material for an interested reader.

Original languageEnglish
Pages (from-to)30-44
Number of pages15
JournalInformation Fusion
Volume55
DOIs
Publication statusPublished - Mar 2020

Bibliographical note

Funding Information:
This research was partly supported by DST Group under the Research Agreement “Classification decisions under uncertainty”.

Funding Information:
This research was partly supported by DST Group under the Research Agreement ?Classification decisions under uncertainty?.

Publisher Copyright:
© 2019

Keywords

  • Bayesian
  • Belief function theory
  • Imprecise probability
  • Imprecision
  • Information fusion
  • Model based classification
  • Possibility functions
  • Random sets
  • Uncertainty

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture

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