LPIS Home Page
Google Search

Title: Lazy Adaptive Multicriteria Planning
Author(s): G. Tsoumakas, D. Vrakas, N. Bassiliades, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (5 pages).
Appeared in: Proc. 16th European Conference on Artificial Intelligence, ECAI 2004, R. Lopez de Mantaras and L. Saitta (Eds.), IOS Press, pp. 693-697, Valencia, Spain, 2004.
Abstract: This paper describes a learning system for the automatic configuration of domain independent planning systems, based on measurable features of planning problems. The purpose of the Lazy Adaptive Multicriteria Planning (LAMP) system is to configure a planner in an optimal way, concerning two quality metrics (i.e. execution speed and plan quality), for a given problem according to user-specified preferences. The training data are produced by running the planner under consideration on a set of problems using all possible parameter configurations and recording the planning time and the plan length. When a new problem arises, (LAMP) extracts the values for a number of domain-expert specified problem features and uses them to identify the k nearest problems solved in the past. The system then performs a multicriteria combination of the performances of the retrieved problems according to user-specified weights that specify the relative importance of the quality metrics and selects the configuration with the best score. Experimental results show that LAMP improves the performance of the default configuration of two already well-performing planning systems in a variety of planning problems.
See also :

        This paper has been cited by the following:

1 P. M. dos Santos, "Selecao de Modelos de Previsao de Series Temporais baseada em Informacoes de Desempenho", Master Thesis, Universidade Federal de Pernambuco, Brazil, 2006
2 R. Prudencio and T. Ludermir, "Aprendizagem Ativa para Selecao de Exemplos em Meta-Aprendizado", 6th Brazilian Meeting on Artificial Intelligence (Encontro Nacional de Inteligencia Artificial - ENIA), 3-6 July 2007, Instituto Militar de Engenharia (IME), Rio de Janeiro, Brazil.
3 R. Prudencio and T. Ludermir, "Active Learning to Support the Generation of Meta-examples", Proc. 17th International Conference on Artificial Neural Networks, ICANN 2007, Porto, Portugal, September 9-13, 2007, pp. 817-826.
4 P. M. Santos, T. B. Ludermir, R.B.C. Prudencio, "Selecting neural network forecasting models using the zoomed-ranking approach", Proceedings - 10th Brazilian Symposium on Neural Networks, SBRN 2008, art. no. 4665910, pp. 165-170
5 R. B.C. Prudencio and T. B. Ludermir, Selective generation of training examples in active meta-learning, International Journal of Hybrid Intelligent Systems 5 (2008) 59–70
6 M. C. P. de Souto, R. B. C. Prudencio, R. G. F. Soares, D. A. S. Araujo, I. G. Costa , T. B. Ludermir, and A. Schliep Ranking and Selecting Clustering Algorithms Using a Meta-learning Approach. Proceedings of the International Joint Conference on Neural Networks, IEEE Computer Society, 2008
7 Nascimento, A.C.A., Prudêncio, R.B.C., de Souto, M.C.P., Costa, I.G., (2009) “Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data, Proc. 19th International Conference on Artificial Neural Networks – ICANN 2009, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II, pp. 20-29
8 Ricardo B. C. Prudêncio, Marcilio C. P. de Souto, Teresa B. Ludermir (2011) Selecting Machine Learning Algorithms Using the Ranking Meta-Learning Approach, In Meta-Learning in Computational Intelligence, Norbert Jankowski, Wlodzislaw Duch and Krzysztof Grabczewski (eds.), pp. 225-243