Abstract
A system's constraints characterizes what that system can do. However, a dynamic environment may require that a system alter its constraints. If feedback about a specific situation is available, a system may be able to adapt by reflecting on its own reasoning processes. Such reflection may be guided not only by explicit representation of the system's constraints but also by explicit representation of the functional role that those constraints play in the reasoning process. We present an operational computer program, Sirrine2 which uses functional models of a system to reason about traits such as system constraints. We further describe an experiment with Sirrine2 in the domain of meeting scheduling.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Lisa Dent, Jesus Boticario, Tom Mitchell, David Sabowski, and John McDermott. A personal learning apprentice. In William Swartout, editor, Proceedings of the 10th National Conference on Artificial Intelligence-AAAI-92, pages 96–103, San Jose, CA, July 1992. MIT Press.
R. E. Fikes and N. J. Nilsson. STRIPS: a new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3-4):189–208, 1971.
Todd Grifth and J. William Murdock. The role of reflection in scientific exploration. In Proceedings of the Twentieth Annual Conference of the Cognitive Science Society, 1998.
Pattie Maes and Robyn Kozierok. Learning interface agents. In Proceedings of the 11th National Conference on Artificial Intelligence-AAAI-93, pages 459–464, Menlo Park, CA, USA, July 1993. AAAI Press.
Eleni Stroulia and Ashok K. Goel. A model-based approach to blame assignment: Revising the reasoning steps of problem solvers. In Proceedings of the National Conference on Artificial Intelligence-AAAI-96, Portland, Oregon, August 1996.
D. E. Wilkins. Can AI planners solve practical problems? Computational Intelligence, 6(4):232–246, 1990.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Murdock, J.W., Goel, A.K. (2001). Learning about Constraints by Reflection. In: Stroulia, E., Matwin, S. (eds) Advances in Artificial Intelligence. Canadian AI 2001. Lecture Notes in Computer Science(), vol 2056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45153-6_13
Download citation
DOI: https://doi.org/10.1007/3-540-45153-6_13
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42144-3
Online ISBN: 978-3-540-45153-2
eBook Packages: Springer Book Archive