Capturing scheduling knowledge from repair experiences

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.

[1]  James A. Hendler,et al.  A Validation-Structure-Based Theory of Plan Modification and Reuse , 1992, Artif. Intell..

[2]  Janet L. Kolodner,et al.  A Process Model of Cased-Based Reasoning in Problem Solving , 1985, IJCAI.

[3]  Kazuo Miyashita,et al.  Adaptive Schedule Repair , 1993, Knowledge-Based Reactive Scheduling.

[4]  Kristian J. Hammond,et al.  Case-Based Planning: Viewing Planning as a Memory Task , 1989 .

[5]  Ray Bareiss,et al.  Exemplar-Based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning , 1990 .

[6]  Manuela Veloso.  Learning by analogical reasoning in general problem-solving , 1992 .

[7]  G. Rand.  Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop , 1982 .

[8]  John P. McDermott,et al.  Toward Automating Recognition of Differing Problem-Solving Demands , 1988, Int. J. Man Mach. Stud..

[9]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[10]  M. Numao,et al.  A scheduling environment for steel-making processes , 1989, [1989] Proceedings. The Fifth Conference on Artificial Intelligence Applications.

[11]  Norman Sadeh,et al.  Look-ahead techniques for micro-opportunistic job shop scheduling , 1992 .

[12]  Alok R. Chaturvedi,et al.  Acquiring implicit knowledge in a complex domain , 1993 .

[13]  David L. Waltz,et al.  Trading MIPS and memory for knowledge engineering , 1992, CACM.

[14]  Steven Minton,et al.  Solving Large-Scale Constraint-Satisfaction and Scheduling Problems Using a Heuristic Repair Method , 1990, AAAI.

[15]  David L. Waltz,et al.  Toward memory-based reasoning , 1986, CACM.

[16]  Michael J. Shaw.  A pattern-directed approach to flexible manufacturing: A framework for intelligent scheduling, learning, and control , 1989 .

[17]  Stephen F. Smith,et al.  Issues in the Design of AI-Based Schedulers - Workshop Report , 1991, AI Mag..

[18]  Bing Liu.  Problem acquisition in scheduling domains , 1993 .

[19]  Steven Minton,et al.  Machine Learning Methods for Planning , 1994 .

[20]  John McDermott,et al.  Usable and reusable programming constructs , 1991 .

[21]  Stephen F. Smith,et al.  Reactive Plan Revision , 1988, AAAI.

[22]  Monte Zweben,et al.  Learning to Improve Constraint-Based Scheduling , 1992, Artif. Intell..

[23]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .