{"title":"Grounded semantics and principle-based analysis for incomplete argumentation frameworks","authors":"Jean-Guy Mailly","doi":"10.1016/j.ijar.2024.109282","DOIUrl":null,"url":null,"abstract":"<div><p>Incomplete Argumentation Frameworks (IAFs) enrich classical abstract argumentation with arguments and attacks whose actual existence is questionable. The usual reasoning approaches rely on the notion of completion, <em>i.e.</em> standard AFs representing “possible worlds” compatible with the uncertain information encoded in the IAF. Recently, extension-based semantics for IAFs that do not rely on the notion of completion have been defined, using instead new versions of conflict-freeness and defense that take into account the (certain or uncertain) nature of arguments and attacks. In this paper, we give new insights on both the “completion-based” and the “direct” reasoning approaches. First, we adapt the well-known grounded semantics to this framework in two different versions that do not rely on completions. After determining that our new semantics are polynomially computable, we provide a principle-based analysis of these semantics, as well as the “direct” semantics previously defined in the literature, namely the complete, preferred and stable semantics. Finally, we also provide new results regarding the satisfaction of principles by the classical “completion-based” semantics.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"175 ","pages":"Article 109282"},"PeriodicalIF":3.2000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0888613X24001695/pdfft?md5=a67ae311f4886d4fd78eaed11ac33dc3&pid=1-s2.0-S0888613X24001695-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Approximate Reasoning","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0888613X24001695","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Incomplete Argumentation Frameworks (IAFs) enrich classical abstract argumentation with arguments and attacks whose actual existence is questionable. The usual reasoning approaches rely on the notion of completion, i.e. standard AFs representing “possible worlds” compatible with the uncertain information encoded in the IAF. Recently, extension-based semantics for IAFs that do not rely on the notion of completion have been defined, using instead new versions of conflict-freeness and defense that take into account the (certain or uncertain) nature of arguments and attacks. In this paper, we give new insights on both the “completion-based” and the “direct” reasoning approaches. First, we adapt the well-known grounded semantics to this framework in two different versions that do not rely on completions. After determining that our new semantics are polynomially computable, we provide a principle-based analysis of these semantics, as well as the “direct” semantics previously defined in the literature, namely the complete, preferred and stable semantics. Finally, we also provide new results regarding the satisfaction of principles by the classical “completion-based” semantics.
期刊介绍:
The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest.
Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning.
Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.