通过实值逻辑进行不确定性推理的基础
Ronald Fagin1, Ryan Riegel2, Alexander Gray3
1International Business Machines (IBM) Almaden Research Center, IBM Research, San Jose, CA 95120.
概括
这项研究建立了实值逻辑的基本原则,对神经符号AI至关重要. 它介绍了一个用于分析逻辑推理的多功能框架,增强对复杂AI推理能力的理解.
科学领域:
- 人工智能的人工智能
- 逻辑和推理的逻辑和推理
- 计算机科学 计算机科学
背景情况:
- 越来越多的人对实值逻辑感兴趣,特别是在AI和神经符号方法中.
- 这些系统中逻辑推理能力的现有定性表征.
研究的目的:
- 为建立实值逻辑系统的正确性和功率提供基础原则.
- 介绍一个丰富的多维句子类和一个声音,完整的公理化.
主要方法:
- 引入描述可能的真值组合的多维句子.
- 开发一个参数化的公理化,涵盖各种实值逻辑 (模糊,加权,概率).
- 线性编程的应用在决策过程中,以确定逻辑含义.
主要成果:
- 一个声音和完整的公理化为一个丰富的多维句子类.
- 强大的完整性结果使真实值组合的精确推断成为可能.
- 在某些实值逻辑中,逻辑含义的决策程序.
结论:
- 这项工作为理解和描述实值逻辑的推理属性提供了一个一般框架.
- 提供了对现有的和新的实值逻辑以及深度学习模型的推理的洞察.
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