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首页> 外文会议>PRICAI'98 : Topics in artificial intelligence >Analyzing the Roles of Problem Solving and Learning in Organizational-Learning Oriented Classifier System
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Analyzing the Roles of Problem Solving and Learning in Organizational-Learning Oriented Classifier System

机译：分析解决问题和学习在面向组织学习的分类器系统中的作用

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This paper analyzes the roles of problem solving and learning in Organizational-learning oriented Classifier System (OCS) from the viewpoint of organizational learning in organization and management sciences, and validates the effectiveness of the roles through the experiments of large scale problems for Printed Circuit Boards (PCBs) re-design in the Computer Aided Design (CAD). OCS is a novel multiagent-based architecture, and is composed of the following four mechanisms: (1) reinforcement learning, (2) rule generation, (3) rule exchange, and (4) organizational knowledge utilization. In this paper, we discuss that the four mechanisms in OCS work respectively as an individual performance/concept learning and an organizational performance/concept learning in organization and management sciences. Through the intensive experiments on the re-design problems of real scale PCBs, the results suggested that four learning mechanisms in individual/organizational levels contribute to finding not only feasible part placements in fewer iterations but also the shorter total wiring length than the one by human experts.
机译：本文从组织学习在组织和管理科学中的角度分析了面向问题的学习和学习在面向组织学习的分类器系统（OCS）中的作用，并通过对印刷电路板大规模问题的实验验证了这些作用的有效性。 （PCB）在计算机辅助设计（CAD）中进行重新设计。 OCS是一种新颖的基于多主体的体系结构，它由以下四个机制组成：（1）强化学习，（2）规则生成，（3）规则交换和（4）组织知识利用。在本文中，我们讨论了OCS的四种机制分别作为组织和管理科学中的个人绩效/概念学习和组织绩效/概念学习工作。通过对实际规模PCB重新设计问题的深入实验，结果表明，在个人/组织级别的四种学习机制不仅有助于找到可行的零件放置方式，而且迭代次数更少，而且总布线长度也比人工布线的长度短。专家。

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