.png){kind=icon}
返回
CAPTURING SCHEDULING KNOWLEDGE FROM REPAIR EXPERIENCES
.png){kind=icon}
PRE
AI
DOI：10.1006/ijhc.1994.1080.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In recent years, there have been a lot of efforts in solving scheduling problems by using the techniques of artificial intelligence (AI). However, through development of a variety of AI-based scheduling systems, it has become well known that eliciting effective problem-solving knowledge from human experts is arduous work, and that human schedulers typically lack the knowledge for solving large and complicated scheduling problems in a sophisticated manner. This paper discusses the characteristics of a scheduling problem and describes prior work on acquiring human schedulers' knowledge in scheduling expert systems. Then, a case-based approach, implemented in a system called CABINS, is presented for capturing human experts' preferential criteria about scheduling quality and control knowledge to speed up problem solving. Through iterative schedule repair, CABINS improves the quality of sub-optimal schedules, and during the process CABINS utilizes past repair experiences for (1) repair tactic selection and (2) repair result evaluation. It is empirically demonstrated in this paper that CABINS can revise a schedule along objectives captured in its case base and can improve the efficiency of the revision process while preserving the quality of a resultant schedule.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
