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CABINS
This project is no longer active.

Head: Katia Sycara
Contact: Michael Rectenwald

Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213

Associated center: CIMDS
Associated lab/group: Case Based Reasoning Lab

For more information, see this project's homepage.

Project Description

We have developed an integrated framework of iterative revision integrated with knowledge acquisition and learning for optimization in ill-structured domains, and implemented it in the CABINS system. The ill-structuredness of both the solution space and the desired objectives make many optimization problems difficult to formalize and costly to solve. In such domains, neither the system nor the human expert possess causal domain knowledge that can be used to guide solution optimization. Current optimization technology requires explicit formulation of a single global optimization criterion to control heuristic search for the optimal solution. Often, however, a user's optimization criteria are subjective, situation dependent, and cannot be expressed in terms of a single global optimization function. In CABINS, situation-dependent user's preferences that guide solution revision are captured in cases along with contextual information. During iterative revision of a solution, cases are exploited for multiple purposes, such as revision action selection, revision result evaluation and recovery from revision failures. Our approach was tested in the domain of job shop scheduling. Extensive experimentation on a benchmark suite of job shop scheduling problems has shown that CABINS

1. is capable of acquiring user optimization preferences and tradeoffs,
2. can improve its own competence through knowledge refinement,
3. is a flexible schedule optimization methodology that produces high quality schedules in both predictive schedule generation and reactive schedule management in response to unexpected events during schedule execution.

Past members

	Name	Title	Email Address
	Michael Rectenwald	Writer/Editor III
	Katia Sycara	Research Professor	katia@cs.cmu.edu

Publications

Note: This list may not be comprehensive. It contains only those publications in the RI publications database. Entries are listed in reverse chronological order.

• Modeling Ill-Structured Optimization Tasks through Cases
  K. Miyashita, K. Sycara, and R. Mizoguchi
  Decision Support Systems: An International Journal, Vol. 17, 1996, pp. 345 - 364. [Abstract]
  Download: pdf [233 KB], ps.gz [100 KB] copyrighted

• CABINS: A Framework of Knowledge Acquisition and Iterative Revision for Schedule Improvement and Reactive Repair
  K. Miyashita and K. Sycara
  Artificial Intelligence Journal, Vol. 76, No. 1-2, July, 1995, pp. 377-426. [Abstract]
  Download: pdf [508 KB], ps.gz [188 KB] copyrighted

• Improving System Performance in Case-Based Iterative Optimization through Knowledge Filtering
  K. Miyashita and K. Sycara
  Proc. of the International Joint Conference on Artificial Intelligence, 1995, pp. 371 - 376. [Abstract]
  Download: pdf [124 KB], ps.gz [45 KB] copyrighted

• Using Case-Based Reasoning to Acquire User Scheduling Preferences that Change over Time
  K. Sycara, D. Zeng, and K. Miyashita
  Proceedings of the Eleventh IEEE Conference on Artificial Intelligence Applications, 1995. [Abstract]
  Download: pdf [162 KB], ps.gz [53 KB] copyrighted

• Learning from failure experiences in case-based schedule repair
  K. Sycara and K. Myashita
  Proceedings of the Tewnty-Seventh Hawaii International Conference on System Sciences, Vol.III: Information Systems: Decision Support and Knowledge-Based Systems, January, 1994, pp. 122 - 131. [Abstract]
  Download: pdf [636 KB] copyrighted

• Case-Based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains
  K. Sycara and K. Miyashita
  Proceedings of the Twelfth National Conference on Artificial Intelligence, 1994. [Abstract]
  Download: pdf [117 KB], ps.gz [52 KB] copyrighted