可能性模型而非逻辑是人类推理的基础

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Minds and Machines Pub Date : 2024-06-04 DOI:10.1007/s11023-024-09662-4
P. N. Johnson-Laird, Ruth M. J. Byrne, Sangeet S. Khemlani
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引用次数: 0

摘要

心智模型理论及其计算机实现导致了一些重要的实验,这些实验表明,没有任何标准逻辑--句法微积分和包括它在内的所有逻辑--可以作为人类推理的基础。该理论用必然性(结论描述的只是前提所指的可能性)取代了有效性(结论在前提为真的所有情况下都是真的)这一逻辑概念。许多推论既必然又有效。但实验表明,个体做出的必然性推论是无效的,例如,很少有人吃牛排或鳎目鱼;因此,很少有人吃牛排。其他一些重要的实验表明,个体拒绝接受非必要但有效的推论,例如:他打了麻药或感觉到了疼痛,但两者都没有;因此,他打了麻药或感觉到了疼痛,或两者都有。逻辑学中没有任何东西能证明拒绝有效推论是合理的:对结论的否定与其前提不一致,而不一致会产生任何结论的有效推论,包括被否定的结论。因此,不一致在逻辑学中是灾难性的。与此相反,模型理论将所有推论都视为可失败的(非单调性),而不一致则具有空模型,结合任何其他前提都只能得到空模型。因此,不一致是局部的。这使得自然语言中的真值比标准逻辑语义中的真值丰富得多;个人可以根据事实和未发生的可能性来验证断言。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Models of Possibilities Instead of Logic as the Basis of Human Reasoning

The theory of mental models and its computer implementations have led to crucial experiments showing that no standard logic—the sentential calculus and all logics that include it—can underlie human reasoning. The theory replaces the logical concept of validity (the conclusion is true in all cases in which the premises are true) with necessity (conclusions describe no more than possibilities to which the premises refer). Many inferences are both necessary and valid. But experiments show that individuals make necessary inferences that are invalid, e.g., Few people ate steak or sole; therefore, few people ate steak. Other crucial experiments show that individuals reject inferences that are not necessary but valid, e.g., He had the anesthetic or felt pain, but not both; therefore, he had the anesthetic or felt pain, or both. Nothing in logic can justify the rejection of a valid inference: a denial of its conclusion is inconsistent with its premises, and inconsistencies yield valid inferences of any conclusions whatsoever including the one denied. So inconsistencies are catastrophic in logic. In contrast, the model theory treats all inferences as defeasible (nonmonotonic), and inconsistencies have the null model, which yields only the null model in conjunction with any other premises. So inconsistences are local. Which allows truth values in natural languages to be much richer than those that occur in the semantics of standard logics; and individuals verify assertions on the basis of both facts and possibilities that did not occur.

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来源期刊
Minds and Machines
Minds and Machines 工程技术-计算机:人工智能
CiteScore
12.60
自引率
2.70%
发文量
30
审稿时长
>12 weeks
期刊介绍: Minds and Machines, affiliated with the Society for Machines and Mentality, serves as a platform for fostering critical dialogue between the AI and philosophical communities. With a focus on problems of shared interest, the journal actively encourages discussions on the philosophical aspects of computer science. Offering a global forum, Minds and Machines provides a space to debate and explore important and contentious issues within its editorial focus. The journal presents special editions dedicated to specific topics, invites critical responses to previously published works, and features review essays addressing current problem scenarios. By facilitating a diverse range of perspectives, Minds and Machines encourages a reevaluation of the status quo and the development of new insights. Through this collaborative approach, the journal aims to bridge the gap between AI and philosophy, fostering a tradition of critique and ensuring these fields remain connected and relevant.
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