概率推理作为多重对齐、统一和搜索的信息压缩：介绍和综述
Probabilistic Reasoning as Information Compression by Multiple Alignment, Unification and Search: An Introduction and Overview
摘要：
本文介绍了概率推理（PR）可以理解为“多重对齐、统一和搜索的信息压缩”（ICMAUS）的思想。在这种情况下，多重比对具有与其在生物信息学中的含义相似但不同的含义，而统一意味着匹配模式的简单合并，其含义与该术语在逻辑中的含义相关但比其含义更简单。一个软件模型，SP 61，已开发的发现和形成的“好”的多重比对，在信息压缩方面进行评估。该模型被描述的轮廓。本文以SP 61模型为例，简要介绍了ICMAUS框架如何对各种PR进行建模，包括：最佳匹配模式识别和信息检索中的PR;一步演绎PR和溯因PR;类层次中的属性继承;推理链（概率决策网络和决策树，以及具有“规则”的PR）;几何类比问题;非单调推理和具有默认值的推理;贝叶斯网络的功能建模。
英文摘要：
This article introduces the idea that probabilistic reasoning (PR) may be understood as "information compression by multiple alignment, unification and search" (ICMAUS). In this context, multiple alignment has a meaning which is similar to but distinct from its meaning in bio-informatics, while unification means a simple merging of matching patterns, a meaning which is related to but simpler than the meaning of that term in logic. A software model, SP61, has been developed for the discovery and formation of 'good' multiple alignments, evaluated in terms of information compression. The model is described in outline. Using examples from the SP61 model, this article describes in outline how the ICMAUS framework can model various kinds of PR including: PR in best-match pattern recognition and information retrieval; one-step 'deductive' and 'abductive' PR; inheritance of attributes in a class hierarchy; chains of reasoning (probabilistic decision networks and decision trees, and PR with 'rules'); geometric analogy problems; nonmonotonic reasoning and reasoning with default values; modelling the function of a Bayesian network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
