Abstract
This paper concerns the design and development of an adaptive planner that is able to adjust its parameters to the characteristics of a given problem and to the priorities set by the user concerning plan length and planning time. This is accomplished through the implementation of the k nearest neighbor machine learning algorithm on top of a highly adjustable planner, called HAP. Learning data are produced by running HAP offline on several problems from multiple domains using all value combinations of its parameters. When the adaptive planner HAP\(_{\textrm{NN}}\) is faced with a new problem, it locates the k nearest problems, using a set of measurable problem characteristics, retrieves the performance data for all parameter configurations on these problems and performs a multicriteria combination, with user-specified weights for plan length and planning time. Based on this combination, the configuration with the best performance is then used in order to solve the new problem. Comparative experiments with the statistically best static configurations of the planner show that HAP\(_{\textrm{NN}}\) manages to adapt successfully to unseen problems, leading to an increased planning performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Vrakas, D., Tsoumakas, G., Bassiliades, N., Vlahavas, I.: Learning rules for Adaptive Planning. In: Proceedings of the 13th International Conference on Automated Planning and Scheduling, Trento, Italy, pp. 82–91 (2003)
Vrakas, D., Tsoumakas, G., Bassiliades, N., Vlahavas, I.: Rule Induction for Automatic Configuration of Planning Systems. Technical report, Dept. of Informatics, Aristotle University of Thessaloniki (2003)
Carbonell, J., Knoblock, C.A., Minton, S.: PRODIGY: An integrated architecture for planning and learning. In: VanLehn, K. (ed.) Architectures for Intelligence, pp. 241–278. Lawrence Erlbaum Associates, Mahwah (1991)
Ambite, J., Knoblock, C., Minton, S.: Learning Plan Rewriting Rules. In: Proceedings of the 5th International Conference on Artificial Intelligence Planning and Scheduling Systems, pp. 3–12. AAAI Press, Menlo Park (2000)
Zimmerman, T., Kambhampati, S.: Learning-Assisted Automated Planning: Looking Back, Taking Stock, Going Forward. AI Magazine 24, 73–96 (2003)
Vrakas, D., Vlahavas, I.: Combining progression and regression in state-space heuristic planning. In: Proceedings of the 6th European Conference on Planning, pp. 1–12 (2001)
Vrakas, D., Vlahavas, I.: A heuristic for planning based on action evaluation. In: Proceedings of the 10th International Conference on Automated Planning and Scheduling, pp. 61–70 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tsoumakas, G., Vrakas, D., Bassiliades, N., Vlahavas, I. (2004). Using the k-Nearest Problems for Adaptive Multicriteria Planning. In: Vouros, G.A., Panayiotopoulos, T. (eds) Methods and Applications of Artificial Intelligence. SETN 2004. Lecture Notes in Computer Science(), vol 3025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24674-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-24674-9_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21937-8
Online ISBN: 978-3-540-24674-9
eBook Packages: Springer Book Archive