Welcome to the home page for CABINS
The Robotics Institute, School of Computer Science
Carnegie-Mellon University, Pittsburgh, PA 15213.
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.
1. Miyashita K. and Sycara, K. "CABINS: A Framework of Knowledge Acquisition and Iterative Revision for Schedule Improvement and Reactive Repair", Artificial Intelligence Journal, Vol. 76, No. 1-2, pp. 377-426, July 1995.
2. Miyashita K. and Sycara, K. "Exploitation of Cases for Schedule Quality Improvement", The Journal of the Japanese Society for Artificial Intelligence, Vol. 9, No. 4, pp. 559-568, 1994.
3. Sycara, K. and Miyashita K. "Case-Based Acquisition of User Preferences for Solution Improvement in Ill-Structured Domains". In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), Seattle, Wash., July 31-August 4, 1994.
4. Miyashita, K., Sycara, K. "Adaptive Case-Based Control of Schedule Revision", In "Intelligent Scheduling", M. Fox, and M. Zweben (eds.), Morgan Kaufmann Publishers, July 1994.
5. Miyashita, K. and Sycara, K. "Learning Control Knowledge through Cases in Schedule Optimization Problems", In Proceedings of the Tenth IEEE Conference on Artificial Intelligence for Applications (CAIA-94), IEEE Society Press, San Antonio, Texas, March 1994.
6. Sycara, K. and Miyashita, K., "Evaluation and Improvement of Schedules According to Interactively Acquired User-Defined Criteria", In Proceedings of the Planning Initiative Workshop, Tucson, AZ., February, 1994.
7. Katia Sycara & Dajun Zeng & Kazuo Miyashita 1995.
"Using Case-Based Reasoning
to Acquire User Scheduling Preferences that Change over Time".
In the Proceedings of the Eleventh IEEE Conference on Artificial Intelligence
Applications (CAIA '95), Los Angeles, 1995.
8. Dajun Zeng and Katia Sycara 1995. "Using case-based reasoning as a reinforcement learning framework for optimization with changing criteria". In the Proceedings of the 7th International Conference on Tools with Artificial Intelligence (ICTAI '95), Washington, D.C., November 5-8, 1995.
9. Miyashita, K. and Sycara, K. "Improving System Performance in Case-Based Iterative Optimization through Knowledge Filtering". In the Proceedings of IJCAI'95.
Dajun.Zeng@cs.cmu.edu (Last updated 2-June-98)