偏好推理的理性与直觉结果:论证视角

K. Čyras
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引用次数: 1

摘要

利用偏好信息进行推理是一种常见的人类活动。由于人类推理建模是人工智能的主要目标之一,因此具有偏好的推理是人工智能各个领域的重要课题,例如知识表示和推理(KR)。论证是KR的一个特殊分支,在其他任务中,它涉及到用偏好建模常识性推理。这里的一个关键问题是,在如何处理优惠问题上缺乏共识。见证这一切的是关于如何用论证的方式将偏好的推理形式化的大量建议。然而,作为一个共性,带有偏好的论证的形式主义倾向于满足各种“理性”推理的标准,尽管人类推理往往不是“理性的”,而似乎是“直觉的”。在本文中,我们研究了几种带有偏好的论证形式如何在一个特定的常识性推理问题背后对人类直觉进行建模。更具体地说,我们提出了一个规则和偏好推理的常识性场景,辅以对人类受访者所做决定的调查,该调查表明了一种“直观”的解决方案,并分析了如何在论证中解决这个问题。我们得出的结论是,大多数关于偏好的论证方法都为问题提供了“理性”的解决方案,并讨论了最近一种产生“直觉”解决方案的形式主义。我们认为,我们的结果需要在偏好论证领域取得进展,以及在人工智能中进一步研究偏好推理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rational versus Intuitive Outcomes of Reasoning with Preferences: Argumentation Perspective
Reasoning with preference information is a common human activity. As modelling human reasoning is one of the main objectives of AI, reasoning with preferences is an important topic in various fields of AI, such as Knowledge Representation and Reasoning (KR). Argumentation is one particular branch of KR that concerns, among other tasks, modelling common-sense reasoning with preferences. A key issue there, is the lack of consensus on how to deal with preferences. Witnessing this is a multitude of proposals on how to formalise reasoning with preferences in argumentative terms. As a commonality, however, formalisms of argumentation with preferences tend to fulfil various criteria of `"rational" reasoning, notwithstanding the fact that human reasoning is often not `"rational", yet seemingly `"intuitive". In this paper, we study how several formalisms of argumentation with preferences model human intuition behind a particular common-sense reasoning problem. More specifically, we present a common-sense scenario of reasoning with rules and preferences, complemented with a survey of decisions made by human respondents that indicates an "intuitive" solution, and analyse how this problem is tackled in argumentation. We conclude that most approaches to argumentation with preferences afford a ``"rational" solution to the problem, and discuss one recent formalism that yields the "intuitive" solution instead. We argue that our results call for advancements in the area of argumentation with preferences in particular, as well as for further studies of reasoning with preferences in AI at large.
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