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Updated: Jul 20, 2025

The Modified Temptation Resistance Task: A Paradigm to Elicit Children's Strategic Lie-telling
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CD-BFT: Canonical Decomposition-Based Belief Functions Transformation in Possibility Theory.

Qianli Zhou, Yong Deng, Ronald R Yager

    IEEE Transactions on Cybernetics
    |August 1, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel belief function transformation to ensure consistent combination rules for possibility mass functions (PossMFs) and consonant mass functions. The new method maintains reversibility and improves information fusion.

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    Area of Science:

    • Decision Theory
    • Artificial Intelligence
    • Information Fusion

    Background:

    • Subjective probability mass functions are often represented by qualitative Possibility Mass Functions (PossMFs).
    • Existing transformations between PossMFs and consonant mass functions lack consistency in combination rules, hindering reversible transformations.

    Purpose of the Study:

    • To propose a novel belief function transformation method.
    • To ensure consistency of combination rules between PossMFs and consonant mass functions.
    • To extend the transformation to possibilistic belief structures.

    Main Methods:

    • A new belief function transformation is proposed, interpretable via Smets' and Pichon's canonical decompositions.
    • The method is validated using combination rule consistency, the least commitment principle, and information fusion applications.

    Main Results:

    • The proposed transformation maintains the consistency of combination rules.
    • Reversibility is achieved between PossMFs and consonant mass functions.
    • The transformation is extended to possibilistic belief structures, clarifying the link between possibilistic and evidential information.

    Conclusions:

    • The novel transformation addresses limitations in existing methods, enabling reliable information fusion.
    • This work provides a new perspective on the relationship between possibilistic and evidential information within belief function frameworks.