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Uncertainty: Overview00:59

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task
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Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task

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Knowledge-oriented semantics modelling towards uncertainty reasoning.

Abdul-Wahid Mohammed1, Yang Xu1, Ming Liu1

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731 People's Republic of China.

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|June 29, 2016
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Summary
This summary is machine-generated.

This study introduces HyProb-Ontology, a novel hybrid probabilistic ontology for machine-to-machine (M2M) communication. It effectively handles both discrete and continuous data, improving reasoning accuracy in smart home applications.

Keywords:
Hybrid probabilistic ontologyM2MMulti-agent systemSmart homeUncertainty reasoning

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Last Updated: Mar 18, 2026

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Ontology Engineering

Background:

  • Machine-to-machine (M2M) communication requires semantic interoperability between heterogeneous systems.
  • Existing ontologies struggle to represent uncertainty inherent in M2M domains.
  • Current probabilistic ontologies lack support for simultaneously handling discrete and continuous data.

Purpose of the Study:

  • To propose a hybrid probabilistic ontology capable of representing both discrete and continuous quantities.
  • To address the information loss caused by quantizing continuous data in M2M applications, particularly in smart homes.
  • To enable more effective and accurate reasoning in uncertain M2M environments.

Main Methods:

  • Developed HyProb-Ontology, a framework specifying distributions over class properties.
  • Introduced conditional Gaussian fuzzification to create a unified Ground Hybrid Probabilistic Model.
  • Enabled simultaneous handling of discrete and continuous distributions within the ontology.

Main Results:

  • The proposed HyProb-Ontology successfully handles distributions over both discrete and continuous quantities.
  • The unified Ground Hybrid Probabilistic Model allows for flexible dependency topologies.
  • Experimental results show exact inference with superior performance compared to classical Bayesian networks.

Conclusions:

  • HyProb-Ontology provides a robust solution for probabilistic reasoning in M2M systems with mixed data types.
  • The framework enhances semantic interoperability and reduces information loss in applications like smart homes.
  • This approach offers a significant advancement over existing probabilistic ontology methods for uncertain M2M reasoning.