Learning to Adapt for Case-Based Design
Learning to Adapt for Case-Based Design
复制标题
学习适应基于案例的设计
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
R. Rowe
中科院分区：
文献类型：
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作者：
N. Wiratunga;Susan Craw;R. Rowe
设计是一项复杂的开放式任务，期望案例库包含所有可能设计的代表是不合理的。因此，对于基于实例的设计系统来说，适应是一种理想的能力，但获取适应知识可能需要付出巨大的努力。本文利用案例库中隐含的知识源，针对与检索到的解相关的不同标准，分别归纳适应知识。这提供了一个学习者委员会，与单个学习者相比，他们的组合建议能够更好地满足设计约束和兼容性要求。本论文的主要重点是评估具体到一般和一般到具体的学习对委员会成员获得的适应知识的影响。为此，我们在一个实际的片剂配方问题上进行了实验，这是一个可分解的设计任务。评估结果表明，与仅检索的CBR系统相比，自适应获得了显著的收益，但表明两种学习偏差对不同分解的子任务都是有益的。
Design is a complex open-ended task and it is unreasonable to expect a case-base to contain representatives of all possible designs. Therefore, adaptation is a desirable capability for case-based design systems, but acquiring adaptation knowledge can involve significant effort. In this paper adaptation knowledge is induced separately for different criteria associated with the retrieved solution, using knowledge sources implicit in the case-base. This provides a committee of learners and their combined advice is better able to satisfy design constraints and compatibility requirements compared to a single learner. The main emphasis of the paper is to evaluate the impact of specific-to-general and general-to-specific learning on adaptation knowledge acquired by committee members. For this purpose we conduct experiments on a real tablet formulation problem which is tackled as a decomposable design task. Evaluation results suggest that adaptation achieves significant gains compared to a retrieve-only CBR system, but shows that both learning biases can be beneficial for different decomposed sub-tasks.
DOI：
10.1007/3-540-45014-9
发表时间：
2000-06
期刊：
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影响因子：
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作者：
Thomas G. Dietterich
通讯作者：
Thomas G. Dietterich