在更高层次的不确定性背景下评估和选择论证
Christian Straßer1, Lisa Michajlova1
1Institute for Philosophy II, Ruhr University Bochum, Bochum, Germany.
Frontiers in artificial intelligence
|June 5, 2023
概括
本研究引入了一个正式的论证框架,用于用更高阶的不确定性进行推理,通过整合概率和演方法来加强人工智能和人类认知,以获得更强大的决策.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 正式逻辑 正式逻辑
背景情况:
- 在不确定的环境中推理对于人类和人工智能来说都至关重要.
- 当概率信息不准确或不存在时,就会出现更高阶的不确定性.
- 正式论证,特别是格的抽象论证,模拟了可击败的推理.
研究的目的:
- 开发一个正式的论证框架,以更高层次的不确定性进行推理.
- 增强现有的概率论证系统的演推理能力.
- 为人工智能和人类认知提供适用于人工智能和人类认知的正式模型.
主要方法:
- 在Haenni的概率论证系统的基础上构建.
- 整合推理论证和攻击表示的演论证.
- 使用抽象论证语义来进行论证选择.
- 定义和研究论证强度的概念.
主要成果:
- 提出了一个新的框架,以更高层次的不确定性进行推理.
- 该系统被证明与正式论证的理性假设保持一致.
- 引入并分析了多种衡量论点强度的方法.
- 该框架提供了一种统一的方法来处理推理中的不确定性.
结论:
- 正式论证提供了一个强大的方法来处理更高阶的不确定性.
- 综合框架在不确定的领域提升了人工智能能力.
- 该模型在理解人类推理过程中具有潜在的应用.
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