| Title: |
Using the k-nearest problems for adaptive multicriteria planning |
| Author(s): |
G. Tsoumakas, D. Vrakas, N. Bassiliades, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (10 pages).
|
| Keywords: |
Machine Learning, Planning, Prediction.
|
| Appeared in: |
Proc. 3rd Hellenic Conference on Artificial Intellligence (SETN '04), G. Vouros and T. Panayiotopoulos (Eds.), Springer-Verlag, LNAI 3025, pp. 132-141, Samos, Greece, 2004.
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| 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 HAPNN 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 HAPNN manages to adapt successfully to unseen problems,
leading to an increased planning performance. |
| See also : |
|
This paper has been cited by the following:
| 1 |
C. D.S. Thompson, "Metareasoning About Propagators for Constraint Satisfaction", Master Thesis, Department of Computer Science University of Saskatchewan, Saskatoon, 2011 |
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