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首页> 外文会议>International joint conference on artificial intelligence;IJCAI-89 >Tuning a Knowledge Base of Refinement Rules To Create Good Circuit Designs
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Tuning a Knowledge Base of Refinement Rules To Create Good Circuit Designs

机译：调整优化规则的知识库以创建良好的电路设计

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The efficiency and optimality of a divide-and-conquer approach to design depends upon the associated hierarchy of implementation goals having sibling subgoals that interact only weakly or not at all. Unfortunately, in many domains the only easily acquirable design refinement knowledge leads to the formation of goal hierarchies that violate this assumption.In this paper, we describe a learning method that incrementally transforms a search-based design system that spends much of its time recovering from the implicit (and mistaken) assumption that subproblems do not interact, into a compiler-like system that decomposes the original design problem into truly non-interacting subproblems. The improved system finds locally optimal solutions to its subproblems, which are composed into globally optimal solutions. By analyzing dependencies, the learning method re-parses a poor design decomposition into one with no subproblem interactions; it then generalizes from the resulting decomposition, adding new refinement rules to the knowledge base.We have implemented a design system called CPS that solves design problems of implementing boolean expressions as gate-level circuits. We have also implemented a learning program called SCALE that incrementally transforms CPS into an optimizing compiler.
机译：分治法设计的效率和最佳性取决于实现目标的相关层次结构，该目标层次结构具有同级子目标，而子目标只是弱交互或根本不交互。不幸的是，在许多领域中，唯一易于获得的设计改进知识导致形成违反此假设的目标层次结构。 在本文中，我们描述了一种学习方法，该方法将花费大量时间从基于子问题不相互作用的隐式（和错误）假设中恢复的基于搜索的设计系统逐步转换为可分解原始设计的类似编译器的系统问题变成真正的非相互作用子问题。改进后的系统为其子问题找到局部最优解，这些局部解被组合为全局最优解。通过分析依赖性，该学习方法将不良的设计分解重新解析为没有子问题交互的分解。然后从结果分解中进行归纳，将新的细化规则添加到知识库中。 我们已经实现了一个称为CPS的设计系统，该系统解决了将布尔表达式实现为门级电路的设计问题。我们还实施了一个名为SCALE的学习程序，该程序将CPS逐步转换为优化的编译器。

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