The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Practical optimization problems such as job-shop scheduling often involve optimization criteria that change over time. Repair-based frameworks have been identified as flexible computational paradigms for difficult combinatorial optimization problems. Since the control problem of repair-based optimization is severe, reinforcement learning (RL) techniques can be potentially helpful. However, some of the fundamental assumptions made by traditional RL algorithms are not valid for repair-based optimization. Case-based reasoning compensates for some of the limitations of traditional RL approaches. We present a case-based reasoning RL approach, implemented in the C/sub A/B/sub I/NS system, for repair-based optimization. We chose job-shop scheduling as the testbed for our approach. Our experimental results show that C/sub A/B/sub I/NS is able to effectively solve problems with changing optimization criteria which are not known to the system and only exist implicitly in a extensional manner in the case base.

Notes

Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems

Associated Lab(s) / Group(s): Case Based Reasoning Lab

Text Reference

Dajun Zeng and Katia Sycara, "Using case-based reasoning as a reinforcement learning framework for optimization with changing criteria," Proceedings of the 7thInternational Conference on Tools with Artificial Intelligence (ICTAI '95), November, 1995, pp. 56 - 62.

BibTeX Reference

@inproceedings{Zeng_1995_2639,
   author = "Dajun Zeng and Katia Sycara",
   title = "Using case-based reasoning as a reinforcement learning framework for optimization with changing criteria",
   booktitle = "Proceedings of the 7thInternational Conference on Tools with Artificial Intelligence (ICTAI '95)",
   pages = "56 - 62",
   month = "November",
   year = "1995",
}

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