{"title":"动态集体论证:构建修正和收缩运算符","authors":"Weiwei Chen, Shier Ju","doi":"10.1016/j.ijar.2024.109234","DOIUrl":null,"url":null,"abstract":"<div><p>Collective argumentation has always focused on obtaining rational collective argumentative decisions. One approach that has been extensively studied in the literature is the aggregation of individual extensions of an argumentation framework. However, previous studies have only examined aggregation processes in static terms, focusing on preserving semantic properties at a given time. In contrast, this paper investigates whether decisions remain rational when the preservation process is dynamic, meaning that it can incorporate new information. To address the dynamic nature of collective argumentation, we introduce the revision and contraction operators. These operators reflect the idea that when an individual or a group learns something new by accepting or rejecting an argument, they have to update their collective decision accordingly. Our study examines whether the order of revising individual opinions and aggregating them affects the final outcome, i.e., whether aggregation and revision commute.</p></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"172 ","pages":"Article 109234"},"PeriodicalIF":3.2000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dynamic collective argumentation: Constructing the revision and contraction operators\",\"authors\":\"Weiwei Chen, Shier Ju\",\"doi\":\"10.1016/j.ijar.2024.109234\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Collective argumentation has always focused on obtaining rational collective argumentative decisions. One approach that has been extensively studied in the literature is the aggregation of individual extensions of an argumentation framework. However, previous studies have only examined aggregation processes in static terms, focusing on preserving semantic properties at a given time. In contrast, this paper investigates whether decisions remain rational when the preservation process is dynamic, meaning that it can incorporate new information. To address the dynamic nature of collective argumentation, we introduce the revision and contraction operators. These operators reflect the idea that when an individual or a group learns something new by accepting or rejecting an argument, they have to update their collective decision accordingly. Our study examines whether the order of revising individual opinions and aggregating them affects the final outcome, i.e., whether aggregation and revision commute.</p></div>\",\"PeriodicalId\":13842,\"journal\":{\"name\":\"International Journal of Approximate Reasoning\",\"volume\":\"172 \",\"pages\":\"Article 109234\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Approximate Reasoning\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0888613X2400121X\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Approximate Reasoning","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0888613X2400121X","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}
Dynamic collective argumentation: Constructing the revision and contraction operators
Collective argumentation has always focused on obtaining rational collective argumentative decisions. One approach that has been extensively studied in the literature is the aggregation of individual extensions of an argumentation framework. However, previous studies have only examined aggregation processes in static terms, focusing on preserving semantic properties at a given time. In contrast, this paper investigates whether decisions remain rational when the preservation process is dynamic, meaning that it can incorporate new information. To address the dynamic nature of collective argumentation, we introduce the revision and contraction operators. These operators reflect the idea that when an individual or a group learns something new by accepting or rejecting an argument, they have to update their collective decision accordingly. Our study examines whether the order of revising individual opinions and aggregating them affects the final outcome, i.e., whether aggregation and revision commute.
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.