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首页> 外文期刊>Neural Network World >NEST: RE-ENGINEERING THE COMPOSITIONAL APPROACH TO RULE-BASED INFERENCE
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NEST: RE-ENGINEERING THE COMPOSITIONAL APPROACH TO RULE-BASED INFERENCE

机译：NEST：重新构建基于规则的推理的组合方法

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摘要

A compositional approach to rule-based inference is now often considered as overtaken by other approaches. We suggest that a few relatively straightforward extensions together with state-of-the-art implementation techniques should upgrade it to a level making it a useful part of today's knowledge engineering inventory. The ideas developed by the authors in mid-90s have recently been incorporated into a new expert system called NEST. In addition to the traditional network of propositions and compositional rules, NEST also supports binary, nominal and numeric attributes used for derivation of proposition weights, logical (no uncertainty) and default (no antecedent) rules, context expressions and integrity constraints. The inference mechanism combines backward and forward chaining. Uncertainty processing (based on Hajek's algebraic theory) allows interval weights interpreted as a union of hypothetical cases, and a novel set of combination functions inspired by neural networks has been added. The system is implemented in two versions: stand-alone and web-based client-server one. A user-friendly editor covering all mentioned features is included.
机译：现在，基于规则的推理的组合方法通常被其他方法所取代。我们建议一些相对简单的扩展以及最新的实现技术应将其升级到一个使之成为当今知识工程清单中有用的部分的水平。作者在90年代中期提出的想法最近被纳入一个称为NEST的新专家系统中。除了传统的命题和组成规则网络之外，NEST还支持用于推导命题权重，逻辑（无不确定性）和默认（无先例）规则，上下文表达式和完整性约束的二进制，名义和数字属性。推理机制结合了反向链接和正向链接。不确定性处理（基于Hajek的代数理论）允许将区间权重解释为假设情况的并集，并且添加了一组受神经网络启发的新型组合函数。该系统有两种版本：独立版本和基于Web的客户端-服务器版本。包括一个涵盖所有提到的功能的用户友好型编辑器。

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