Approximations of system W for inference from strongly and weakly consistent belief bases

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

Abstract

In this article, we investigate approximations of the inductive inference operator system W that has been shown to exhibit desirable inference properties and to extend both system Z, and thus rational closure, and c-inference. For versions of these inference operators that are extended to also cover inference from belief bases that are only weakly consistent, we first show that extended system Z and extended c-inference are captured by extended system W. Then we introduce general functions for generating inductive inference operators: the combination of two inductive inference operators by union, and the completion of an inductive inference operator by an arbitrary set of axioms. We construct the least inductive inference operator extending system Z and c-inference that is closed under system P and show that it is still strictly extended by extended system W. Furthermore, we introduce an inductive inference operator that strictly extends extended system W and that is strictly extended by lexicographic inference. This leads to a comprehensive map of inference relations between rational closure and extended c-inference on the one side and lexicographic inference on the other side with extended system W and its approximations at its centre, where all relationships also hold for the unextended versions.

从强一致和弱一致信念基础进行推理的系统 W 近似值
在这篇文章中,我们研究了归纳推理算子系统 W 的近似值,这些近似值已被证明展示了理想的推理特性,并扩展了系统 Z,从而扩展了有理闭包和 c 推理。对于这些推理算子的扩展版本,我们首先证明扩展的系统 Z 和扩展的 c-推理是由扩展的系统 W 所捕获的。然后,我们引入了生成归纳推理算子的一般函数:两个归纳推理算子的结合(union)和归纳推理算子的完成(completion)。我们构造了最小的归纳推理算子,它扩展了系统 Z 和 c-推理,在系统 P 下是封闭的,并证明它仍然被扩展系统 W 严格扩展。这样,我们就得到了以扩展系统 W 及其近似为中心的理性封闭和扩展 c 推理与词法推理之间推理关系的综合映射,其中所有关系对于未扩展版本也是成立的。
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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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