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Induction and plausibility: A formal approach from the standpoint of artificial intelligence.

机译：归纳和合理性：从人工智能的角度来看的一种正式方法。

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摘要

The purpose of this work is to analyze the notion of induction, conceived as the class of rational non-truth preserving inferences, from the point of view of the nonmonotonic logical tradition raised inside the field of Artificial Intelligence (AI) in the last twenty-five years. By centering our efforts around what has been called the problem of description of induction, we intend to explicate (in Carnap's sense) the notion of induction through a purely descriptive and consequently justificatory-free approach to induction. Of fundamental importance for this enterprise is the notion of plausibility, here understood as the same as Carnap's notion of pragmatical probability. By providing a representational formal analysis of the notion of induction, we also aim to explicate something akin to the ordinary notion of plausibility. One of the main features of this relationship between induction and plausibility concerns the two most basic approaches one can take when dealing with the contradictions that are sure to arise from the use of inductive inferences. These skeptical and credulous approaches to induction, as we have named them, give rise to two different plausibility notions which bear important relations to a domain of formal logic closely connected with AI, the field of paraconsistent and paracomplete logic: while the skeptical plausibility is a paracomplete notion, the credulous plausibility is a paraconsistent one. At the basis of our formal work is the result that in opposition to the formal approaches developed in philosophy, the justificatory-free aspect we are looking for is already present in most nonmonotonic logics. We pick two of these formalisms---Reiter's default logic and Pequeno's paraconsistent default logics---and extend them in such a way as to obtain a system able to perform the task a descriptive logic of induction is supposed to perform. To complete our formal work, we also develop a so-called paranormal (i.e., simultaneously paracomplete and paraconsistent) modal logic to represent the two notions of plausibility and act in conjunction with the mentioned nonmonotonic logic. In this way, our work is also a contribution to the field of modal logic. In order to show the applicability of our system, we present a formalization of the so-called abductive reasoning and hypothetico-deductive reasoning applied to the problem of confirmation of hypotheses in philosophy of science.
机译：这项工作的目的是从过去20年代在人工智能（AI）领域内提出的非单调逻辑传统的角度，分析归纳的概念，即归纳为有理非真理性保留推理的一类。 5年。通过将我们的工作集中在所谓的归纳描述问题上，我们打算（通过Carnap的观点）通过一种纯粹描述性的，因此没有正当理由的归纳方法来阐明归纳的概念。对于这个企业而言，最重要的是似真性的概念，在这里理解为与卡尔纳普的实用概率的概念相同。通过对归纳概念进行代表性的形式化分析，我们还旨在阐明类似于普通似然概念的内容。归纳性与合理性之间这种关系的主要特征之一是，在处理肯定会因归纳推理而引起的矛盾时，可以采用的两种最基本的方法。正如我们所命名的，这些怀疑和轻信的归纳方法产生了两个不同的似真性概念，它们与与AI密切相关的形式逻辑领域（超一致逻辑和超完全逻辑领域）具有重要关系：而怀疑似然性是在超完全概念中，可信的可信度是超一致的。作为我们形式工作的基础的结果是，与哲学中发展起来的形式方法相反，我们所寻找的无辩证性方面已经存在于大多数非单调逻辑中。我们选择其中两种形式主义-Reiter的默认逻辑和Pequeno的超常默认逻辑-并以某种方式扩展它们，以使系统能够执行描述性的归纳逻辑。为了完成我们的正式工作，我们还开发了一种所谓的超自然（即同时超完全和超一致）模态逻辑，以表示两个合理性概念并与上述非单调逻辑一起起作用。这样，我们的工作也为模态逻辑领域做出了贡献。为了显示我们系统的适用性，我们提出了一种形式化的所谓归纳推理和假设演绎推理，用于推理科学哲学中的假设确认问题。

著录项

  • 作者

    Silvestre, Ricardo Sousa.;

  • 作者单位

    Universite de Montreal (Canada).;

  • 授予单位 Universite de Montreal (Canada).;
  • 学科 Philosophy.
  • 学位 Ph.D.
  • 年度 2005
  • 页码 257 p.
  • 总页数 257
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 哲学理论;
  • 关键词

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