Integration of evolutionary prejudices in Dempster-Shafer theory

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Florence Dupin de Saint-Cyr, Francis Faux
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引用次数: 0

Abstract

This paper deals with belief change in the framework of Dempster-Shafer theory in the context where an agent has a prejudice, i.e., a priori knowledge about a situation. Our study is based on a review of the literature in the social sciences and humanities. Our framework relies on the claim that prejudices and evidences should be dealt with separately because of their very different natures (prejudices being at the meta level, governing the evolution of beliefs). Hence, the cognitive state of an agent is modeled as a pair whose components reflect its prejudices and uncertain beliefs. We propose a general formalism for encoding the evolution of this pair when new information arrives, this is why the study is related to Dempster's revision. Several cases of prejudice are described: the strong persistent prejudice (which never evolves and forbids beliefs to change), the prejudice that is slightly decreasing each time a belief contradicts it, etc. A general example with several prejudices and complex masses illustrates our approach.
邓普斯特-谢弗理论中进化偏见的整合
本文在Dempster-Shafer理论的框架下,研究行为人对某一情境具有偏见(即先验知识)情况下的信念变化。我们的研究是基于对社会科学和人文科学文献的回顾。我们的框架依赖于偏见和证据应该分开处理的主张,因为它们的性质非常不同(偏见处于元层面,支配着信仰的演变)。因此,智能体的认知状态被建模为一对,其组成部分反映了它的偏见和不确定的信念。我们提出了一个通用的形式来编码这对在新信息到来时的进化,这就是为什么这项研究与Dempster的修订有关。书中描述了几种偏见的例子:强烈而持久的偏见(这种偏见永远不会进化,并禁止信仰改变),每当信仰与之矛盾时,偏见就会略微减少,等等。一个带有若干偏见和复杂质量的一般例子说明了我们的方法。
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
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.
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