Abstract
This paper explores the formulation of image interpretation as a Markov Decision Process (MDP) problem, highlighting the important assumptions in the MDP formulation. Furthermore state abstraction, value function and action approximations as well as lookahead search are presented as necessary solution methodologies. We view the task of image interpretation as a dynamic control problem where the optimal vision operator is selected responsively based on the problem solving state at hand. The control policy, therefore, maps problem-solving states to operators in an attempt to minimize the total problem-solving time while reliably interpreting the image. Real world domains, like that of image interpretation, usually have incredibly large state spaces which require methods of abstraction in order to be manageable by today’s information processing systems. In addition an optimal value function (V*) used to evaluate state quality is also generally unavailable requiring approximations to be used in conjunction with state abstraction. Therefore, the performance of the system is directly related to the types of abstractions and approximations present.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bulitko, V., Caelli, T., McNabb, D. 2000. Forestry Information Management System (FIMS): An Introduction. Technical Report. University of Alberta.
Draper, B., Bins, J., Baek, K. 2000. ADORE: Adaptive Object Recognition, Videre, 1(4):86–99.
Good, I.J. 1971. Twenty-seven Principles of Rationality. In Foundations of Statistical Inference, Godambe V.P., Sprott, D.A. (editors), Holt, Rinehart and Winston, Toronton.
Gougeon, F.A. 1993. Individual Tree Identification from High Resolution MEIS Images, In Proceedings of the International Forum on Airborne Multispectral Scanning for Forestry and Mapping, Leckie, D.G., and Gillis, M.D. (editors), pp. 117–128.
Hsu, F.H., Campbell, M.S., Hoane, A.J.J. 1995. Deep Blue System Overview. Proceedings of the 9th ACM Int. Conf. on Supercomputing, pp. 240–244.
Korf, R.E. 1990. Real-time heuristic search. Artificial Intelligence, Vol. 42, No. 2–3, pp. 189–211.
Larsen, M. and Rudemo, M. 1997. Using ray-traced templates to find individual trees in aerial photos. In Proceedings of the 10th Scandinavian Conference on Image Analysis, volume 2, pages 1007–1014.
Newborn, M. 1997. Kasparov vs. Deep Blue: Computer Chess Comes Out of Age. Springer-Verlag.
Pollock, R.J. 1994. A Model-based Approach to Automatically Locating Tree Crowns in High Spatial Resolution Images. Image and Signal Processing for Remote Sensing. Jacky Desachy, editor.
Schaeffer, J., Culberson, J., Treloar, N., Knight, B., Lu, P., Szafron, D. 1992. A World Championship Caliber Checkers Program. Artificial Intelligence, Volume 53, Number 2–3, pages 273–290.
Sutton, R.S., Barto, A.G., 2000. Reinforcement Learning: An Introduction. MIT Press.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Levner, I., Bulitko, 1., Madani, O., Greiner, R. (2002). Performance of Lookahead Control Policies in the Face of Abstractions and Approximations. In: Koenig, S., Holte, R.C. (eds) Abstraction, Reformulation, and Approximation. SARA 2002. Lecture Notes in Computer Science(), vol 2371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45622-8_23
Download citation
DOI: https://doi.org/10.1007/3-540-45622-8_23
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43941-7
Online ISBN: 978-3-540-45622-3
eBook Packages: Springer Book Archive