用可能性理论解决人工智能不确定性难题，避免传统方法的逻辑陷阱。
Resolving Zadehs Paradox Axiomatic Possibility Theory as a Foundation for Reliable Artificial Intelligence
- 基于可能性与必要性测度构建逻辑自洽的不确定性处理框架
- 在医学诊断案例中成功处理矛盾数据，避免达摩斯悖论的逻辑错误
- 适合关注可信人工智能、推理机制的学者与工程师阅读
本文主张，解决人工智能不确定性危机的关键在于可能性理论，特别是Bychkov提出的公理化方法。不同于对德姆斯特规则的修补尝试，该方法从零开始构建逻辑一致且数学严谨的不确定性处理基础，采用可能性与必要性测度的双重工具。通过对比概率、证据与可能性三种范式，以经典医学诊断困境为例，证明可能性理论能正确处理矛盾信息，避免达摩斯证据理论（DST）的逻辑陷阱，使形式推理更接近自然智能的思维逻辑。
原文摘要 · Abstract (English)
This work advances and substantiates the thesis that the resolution of this crisis lies in the domain of possibility theory, specifically in the axiomatic approach developed in Bychkovs article. Unlike numerous attempts to fix Dempster rule, this approach builds from scratch a logically consistent and mathematically rigorous foundation for working with uncertainty, using the dualistic apparatus of possibility and necessity measures. The aim of this work is to demonstrate that possibility theory is not merely an alternative, but provides a fundamental resolution to DST paradoxes. A comparative analysis of three paradigms will be conducted probabilistic, evidential, and possibilistic. Using a classic medical diagnostic dilemma as an example, it will be shown how possibility theory allows for correct processing of contradictory data, avoiding the logical traps of DST and bringing formal reasoning closer to the logic of natural intelligence.
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