Capturing scheduling knowledge from repair experiences

✍ Scribed by Kazuo Miyashita; Katia Sycara; Riichiro Mizoguchi

• Publisher: Elsevier Science
• Year: 1994
• Tongue: English
• Weight: 791 KB
• Volume: 41
• Category: Article
• ISSN: 1071-5819
• DOI: 10.1006/ijhc.1994.1080

✦ Synopsis

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.