{"title":"Defining defense and defeat in abstract argumentation from scratch – A generalizing approach","authors":"Lydia Blümel , Markus Ulbricht","doi":"10.1016/j.artint.2025.104456","DOIUrl":null,"url":null,"abstract":"<div><div>We propose a general framework to investigate semantics of Dung-style argumentation frameworks (AFs) by means of a generic defeat notion formalized by <em>refute operators</em>.The main idea underlying our approach is that, given a refute operator <em>δ</em>, counter-parts to all classical semantics can be deduced in a natural way. We demonstrate how classical as well as recent proposals can be captured by our approach when utilizing suitable refute operators. In addition, we showcase how our general scheme can be employed to propose novel semantics in a systematic manner. This results in what we call <em>cooperative semantics</em> which stem from a novel refute operator we will introduce. We perform an in-depth investigation of basic properties of refute operators and to which extent the induced semantics inherit desirable properties from it. Among others, we show under which conditions i) a counterpart to Dung’s fundamental lemma can be inferred, ii) the generalized version of the grounded extension is unique, or iii) the generalized version of stable semantics does not collapse. Moreover, we contribute to a principle-based study of AF semantics by discussing properties tailored to assess the behavior of different refute operators. This includes an investigation of means to compare refute operators in terms of their aggressiveness. Finally, we conclude the study by reporting computational complexity results for basic reasoning tasks which hold in our general framework.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"352 ","pages":"Article 104456"},"PeriodicalIF":4.7000,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0004370225001754","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/12/5 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
We propose a general framework to investigate semantics of Dung-style argumentation frameworks (AFs) by means of a generic defeat notion formalized by refute operators.The main idea underlying our approach is that, given a refute operator δ, counter-parts to all classical semantics can be deduced in a natural way. We demonstrate how classical as well as recent proposals can be captured by our approach when utilizing suitable refute operators. In addition, we showcase how our general scheme can be employed to propose novel semantics in a systematic manner. This results in what we call cooperative semantics which stem from a novel refute operator we will introduce. We perform an in-depth investigation of basic properties of refute operators and to which extent the induced semantics inherit desirable properties from it. Among others, we show under which conditions i) a counterpart to Dung’s fundamental lemma can be inferred, ii) the generalized version of the grounded extension is unique, or iii) the generalized version of stable semantics does not collapse. Moreover, we contribute to a principle-based study of AF semantics by discussing properties tailored to assess the behavior of different refute operators. This includes an investigation of means to compare refute operators in terms of their aggressiveness. Finally, we conclude the study by reporting computational complexity results for basic reasoning tasks which hold in our general framework.
期刊介绍:
The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.