Artificial Intelligence and Operations Research: Challenges and Opportunities in Planning and Scheduling
Abstract
The objective of this effort was to research technology, tools, and techniques to support more efficient techniques for solving hard computational problems. Both the Artificial Intelligence (Al) community and the Operations Research (OR) community are interested in developing techniques for solving hard combinatorial problems, in particular in the domain of planning and scheduling. Al approaches encompass a rich collection of knowledge representation formalisms for dealing with a wide variety of real-world problems. OR based techniques have demonstrated the ability to identify optimal and locally optimal solutions for well-defined problem spaces. In general, however, OR solutions are restricted to rigid models with limited expressive power. Al techniques, on the other hand, provide richer and more flexible representations of real-world problems, supporting efficient constraint-based reasoning mechanisms as well as mixed initiative frameworks, which allow the human expertise to be in the loop. The challenge lies in providing representations that are expensive enough to describe real-world problems and at the same time guaranteeing good and fast solutions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 2001
- Accession Number
- ADA389356
Entities
People
- Carla Gomes
Organizations
- Cornell University