通过分类正确度有效处理了决策表的不一致性，采用启发式算法，挖掘出满足给定精确度的最简产生式规则知识。
We deal with the inconsistency through classification accuracy, using heuristic algorithms we can get a set of minimal productive rules satisfying the given classification accuracy.
此外，启发式知识应加以开发去指导控制的动态层次分布。
Besides, some heuristic knowledge should be developed to guide the dynamic hierarchical distribution of the control.
神经元网络启发式的并行、分布特征和可学习性为专家系统的知识表达和获取、不确定性推理提供了新的途径。
The parallelism, distribution and capability of learning of neural net heuristics provide a new way for knowledge acquisition, knowledge representation and uncertainty reasoning in expert systems.
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