渐进式语义的推理攻击关系

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Argument & Computation Pub Date : 2023-04-20 DOI:10.3233/aac-220010
Nir Oren, Bruno Yun
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引用次数: 0

摘要

渐进语义以加权论证框架作为输入,输出每个论证的最终可接受度,不同的语义以不同的方式执行计算。在这项工作中,我们考虑了攻击推理问题。也就是说,给定一个渐进语义,一组具有相关初始权重的参数,以及与每个参数相关的最终理想可接受度,我们试图确定是否存在一组针对这些参数的攻击,以便我们可以获得这些可接受度。我们工作的主要贡献是证明了相关的决策问题,即是否存在一组允许给定初始权重的最终可接受度发生的攻击,对于加权h分类器和基于卡片的语义来说是np完全的,并且对于加权基于最大值的语义来说是多项式的,甚至对于问题的完整版本(其中所有初始权重和最终可接受度都是已知的)。然后,我们简要讨论如何修改该决策问题以找到攻击本身,并通过检查部分问题来得出结论,其中并非所有初始权重或最终可接受度都是已知的。
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Inferring attack relations for gradual semantics
A gradual semantics takes a weighted argumentation framework as input and outputs a final acceptability degree for each argument, with different semantics performing the computation in different manners. In this work, we consider the problem of attack inference. That is, given a gradual semantics, a set of arguments with associated initial weights, and the final desirable acceptability degrees associated with each argument, we seek to determine whether there is a set of attacks on those arguments such that we can obtain these acceptability degrees. The main contribution of our work is to demonstrate that the associated decision problem, i.e., whether a set of attacks can exist which allows the final acceptability degrees to occur for given initial weights, is NP-complete for the weighted h-categoriser and card-based semantics, and is polynomial for the weighted max-based semantics, even for the complete version of the problem (where all initial weights and final acceptability degrees are known). We then briefly discuss how this decision problem can be modified to find the attacks themselves and conclude by examining the partial problem where not all initial weights or final acceptability degrees may be known.
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来源期刊
Argument & Computation
Argument & Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
4.10
自引率
7.10%
发文量
8
期刊最新文献
Argumentation with justified preferences Abstract argumentation with conditional preferences Argumentative explanations for pattern-based text classifiers Inferring attack relations for gradual semantics
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