一个完整的本体知识库的推理机制必须能够支持这种基于规则的推理。
That means a whole ontology knowledge base needs to be able to support the reasoning based on rules.
推理方法包括用遗传算法进化推理，基于事例的推理以及基于规则的推理。
The reasoning methods include rule-based reasoning, case-based reasoning and generic algorithm.
指出现有信度决策树中推理算法的不足之处，给出了一种新的基于规则的推理算法。
This paper pointed out the shortages of reasoning algorithms of the present belief decision trees and then proposed a new reasoning algorithm.
传统的钻井专家系统采用基于规则的推理，对于难以规则化的典型案例不能够充分利用。
The traditional drilling expert system is based on rule, and the typical cases that are difficultly ruled could not be used enough.
多数故障诊断专家系统采用单一的推理机制，或者基于规则的推理，或者基于事例的推理。
Most fault diagnosis expert system adopted single reasoning mechanism, or rule-based reasoning or case-based reasoning.
基于规则的推理（RBR）和基于案例的推理（CBR）则是知识工程中两类实用有效的推理方式。
Rule-based and case-based reasoning are two important efficient reasoning methods in artificial intelligence field.
本文通过研究采掘领域的专家知识，构造了采掘计划的规则库，并设计了基于产生式规则的推理程序。
Through studying the expert knowledge of mining scheduling, this paper constructed a mining rule base for mining scheduling, and designed a ratiocinate program based on production rules.
推理机采用了基于规则的推理，花境设计时的推理实现了不同的设计要素对花境景观设计的不同重要性。
Reasoning engine adopts the reason according to add regularly, realizing the different importance of different design factors in the process design.
整个网络既有神经网络的学习能力，又有模糊系统的基于规则的推理能力，特别是对子类的自动聚类能力。
The whole network has not only the learning ability to neural network, but also the logic ability to fuzzy system based on rules, especially the automatic clustering ability to sub-class.
文章提出了毛纺织工艺设计智能化模型，通过基于案例(CBR)和基于规则的推理(rbr)以及人工神经网络(ANN)等关键技术的引入，提高了系统解决实际问题的能力。
This paper presents the wool textile process intelligent design model (WTPIDM), by the introduction of CBR, RBR, ANN technologies, improving the system capability to solve the problems.
提出了基于知识的毛纺产品工艺设计智能模型，通过基于案例（C BR）和基于规则的推理（RBR）以及人工神经网络（ANN）等关键技术的引入，提高了系统解决实际问题的能力。
This paper presents the wool textile process intelligent design model(WTPIDM), by the introduction of CBR, RBR, ANN technologies, improving the system capability to solve the problems.
语义技术用存在论(ontology)表示意义，并通过这些存在论中表示的关系、规则、逻辑和条件来提供推理。
Semantic technologies represent meaning using ontologies and provide reasoning through the relationships, rules, logic, and conditions represented in those ontologies.
有状态规则的例子包括大部分事件相关规则和一些推理规则的使用。
Examples of stateful rules include most event correlation rules and some USES of inference rules.
规则可以增强业务策略、制定决策、或从现有的数据中推理出新的数据。
A rule can enforce business policy, make a decision, or infer new data from existing data.
可从推理获益的用例是那些设计到很多相互依赖的规则的用例，这些规则的执行顺序必须依赖于数据而不是预先确定的。
Use cases that benefit from inferencing are those that involve many interdependent rules whose execution orders must depend upon data instead of being predetermined.
为了实现实验操作的正确性检查，采用产生式系统定义实验规则，使用正向推理策略进行规则匹配。
To implement the check-up of the operations, production system is used to define the experimental rules, and forward reasoning strategy is used for rule match.
推理规则实现演绎法、归纳法、prolog样式的统一或其他人工智能(artificial intelligence,AI)样式的规则。
Inference rules implement forward inferencing, backward chaining, Prolog-style unification or other artificial intelligence (ai) -style rules.
最后采用谓词逻辑和产生式表示法相结合的方法作为知识库中的推理规则描述方法。
At last predicate logic is combined with production KR, as which a method to describe the reasoning rule in the knowledge base.
在语义网络的接近顶层，人们可以发现推理—通过规则的对数据的推理。
Near the top of the Semantic Web stack one finds inference - reasoning over data through rules.
结果表明，与传统的基于规则推理(rbr)相比，CBR具有效率高且有自学习能力的优点。
The case demonstrates that CBR has the characteristics of high efficiency and self learning with contrast to conventional Rule Based Reasoning (RBR).
符号是客观世界的抽象表示，而推理和思考，就是按既定规则对它进行操作。
Reasoning and thinking involves manipulation, according to established rules, of symbols which are abstract representations of the real external world.
专家系统用规则来代表专家，推理并得到合理的解决方案。
Expert systems use rules to represent experts, reasoning in solving problems.
提出了一种基于粗集的缺省规则挖掘模型，以利于在信息不完备情况下进行推理和决策。
A rough set model to mine default rules was presented in order to reason and solve the decision question with incomplete information.
对归结原理的基本概念与推理规则进行了讨论，并在此基础上通过实例探讨了归结推理方法在数学定理证明中的应用。
It discusses carefully the basic concepts and inference rule of the resolution principle. According to the discussion, resolution method is used to prove a mathematical theorem through a example.
有关推理依赖项的更多信息，请参见推理规则。
For more information about inferred dependents, see Inference Rules.
介绍了专家系统的具体实现过程，对基于规则、基于案例、基于模型以及混合推理的推理机制进行了研究。
It introduces the concrete realization course of the expert system and traverses the inference mechanism based on rule, case, model and hybrid reasoning.
因而，它应作为推理操作的基本规则。
They must be considered as basic rules of inferential operation.
因而，它应作为推理操作的基本规则。
They must be considered as basic rules of inferential operation.
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