论文标题
针对单个人类推理的定量符号方法
A Quantitative Symbolic Approach to Individual Human Reasoning
论文作者
论文摘要
推理的认知理论是关于了解人类如何从一组前提中得出结论。从假设的思想开始，我们感兴趣，这是基本日常语言背后的含义，以及我们如何推荐它们。一个广泛研究的主题是认知理论是否可以说明典型的推理任务，并通过自己的经验实验确认。本文采用了不同的观点，我们没有提出理论，而是从文献中获取发现，并展示了在逻辑框架内形式化为认知原理的这些方法，可以建立一个定量的推理概念，我们称之为合理性。为此，我们采用了非单调推理和计算机科学的技术，即解决了一种称为“答案集编程”（ASP）的范式。最后，我们可以在ASP中使用合理的推理来测试现有实验的效果，并解释不同的多数反应。
Cognitive theories for reasoning are about understanding how humans come to conclusions from a set of premises. Starting from hypothetical thoughts, we are interested which are the implications behind basic everyday language and how do we reason with them. A widely studied topic is whether cognitive theories can account for typical reasoning tasks and be confirmed by own empirical experiments. This paper takes a different view and we do not propose a theory, but instead take findings from the literature and show how these, formalized as cognitive principles within a logical framework, can establish a quantitative notion of reasoning, which we call plausibility. For this purpose, we employ techniques from non-monotonic reasoning and computer science, namely, a solving paradigm called answer set programming (ASP). Finally, we can fruitfully use plausibility reasoning in ASP to test the effects of an existing experiment and explain different majority responses.
